Overview

Dataset statistics

Number of variables103
Number of observations32
Missing cells94
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.9 KiB
Average record size in memory828.0 B

Variable types

DateTime1
Numeric14
Categorical88

Alerts

civil_conflicts is highly correlated with state_intervention and 10 other fieldsHigh correlation
state_intervention is highly correlated with civil_conflicts and 8 other fieldsHigh correlation
conflict_between_states is highly correlated with totalHigh correlation
total is highly correlated with civil_conflicts and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with state_intervention and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with civil_conflicts and 11 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with civil_conflicts and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with civil_conflicts and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with civil_conflicts and 11 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with civil_conflicts and 11 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with civil_conflicts and 8 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with civil_conflicts and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with civil_conflicts and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with civil_conflicts and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] and 3 other fieldsHigh correlation
civil_conflicts is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] and 6 other fieldsHigh correlation
state_intervention is highly correlated with total and 7 other fieldsHigh correlation
conflict_between_states is highly correlated with totalHigh correlation
total is highly correlated with state_intervention and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with state_intervention and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with state_intervention and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with civil_conflicts and 9 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with civil_conflicts and 8 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with state_intervention and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with civil_conflicts and 11 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with civil_conflicts and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with civil_conflicts and 11 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with civil_conflicts and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with civil_conflicts and 10 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] and 5 other fieldsHigh correlation
civil_conflicts is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] and 2 other fieldsHigh correlation
state_intervention is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] and 3 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] and 2 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] and 6 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with civil_conflicts and 2 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] and 2 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with state_intervention and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with state_intervention and 7 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] and 1 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] and 4 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with civil_conflicts and 5 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with civil_conflicts and 6 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]High correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] is highly correlated with Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 80 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 76 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 77 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
conflict_between_states is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 74 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 76 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 76 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 78 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 74 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 74 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 75 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 76 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 78 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 76 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] is highly correlated with Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] and 86 other fieldsHigh correlation
year is highly correlated with civil_conflicts and 101 other fieldsHigh correlation
civil_conflicts is highly correlated with year and 90 other fieldsHigh correlation
state_intervention is highly correlated with year and 90 other fieldsHigh correlation
conflict_between_states is highly correlated with year and 88 other fieldsHigh correlation
total is highly correlated with year and 94 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] is highly correlated with year and 92 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] is highly correlated with year and 87 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] is highly correlated with year and 92 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] is highly correlated with year and 90 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] is highly correlated with year and 92 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] is highly correlated with year and 92 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] is highly correlated with year and 93 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] is highly correlated with year and 94 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] is highly correlated with year and 94 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] is highly correlated with year and 95 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] is highly correlated with year and 91 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU] is highly correlated with year and 91 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA] is highly correlated with year and 94 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA] is highly correlated with year and 96 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN] is highly correlated with year and 99 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN] is highly correlated with year and 98 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS] is highly correlated with year and 98 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA] is highly correlated with year and 96 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR] is highly correlated with year and 98 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] is highly correlated with year and 101 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] is highly correlated with year and 101 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] is highly correlated with year and 101 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] is highly correlated with year and 101 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] is highly correlated with year and 99 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN] is highly correlated with year and 89 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA] is highly correlated with year and 96 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR] is highly correlated with year and 99 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] is highly correlated with year and 101 other fieldsHigh correlation
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] is highly correlated with year and 101 other fieldsHigh correlation
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] has 9 (28.1%) missing values Missing
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] has 1 (3.1%) missing values Missing
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] has 3 (9.4%) missing values Missing
Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS] has 4 (12.5%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] has 7 (21.9%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] has 6 (18.8%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] has 6 (18.8%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] has 5 (15.6%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] has 6 (18.8%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] has 6 (18.8%) missing values Missing
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] has 6 (18.8%) missing values Missing
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] is uniformly distributed Uniform
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] is uniformly distributed Uniform
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] is uniformly distributed Uniform
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] is uniformly distributed Uniform
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] is uniformly distributed Uniform
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] is uniformly distributed Uniform
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] is uniformly distributed Uniform
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] is uniformly distributed Uniform
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN] is uniformly distributed Uniform
Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD] is uniformly distributed Uniform
year has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN] has unique values Unique
GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - India [IND] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN] has unique values Unique
GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN] has unique values Unique
Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN] has unique values Unique
Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN] has unique values Unique
Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN] has unique values Unique
Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD] has unique values Unique

Reproduction

Analysis started2022-06-24 14:29:14.449590
Analysis finished2022-06-24 14:30:03.768813
Duration49.32 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

year
Date

HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
Minimum1970-01-01 00:00:00.000001
Maximum1970-01-01 00:00:00.000002
2022-06-24T16:30:03.870254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:03.975277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

civil_conflicts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.21875
Minimum23
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:04.076605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile25.2
Q128
median31
Q334.25
95-th percentile44.9
Maximum48
Range25
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.121007586
Coefficient of variation (CV)0.1899827767
Kurtosis1.045391895
Mean32.21875
Median Absolute Deviation (MAD)3
Skewness1.153251821
Sum1031
Variance37.46673387
MonotonicityNot monotonic
2022-06-24T16:30:04.177628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
324
12.5%
284
12.5%
293
9.4%
273
9.4%
313
9.4%
303
9.4%
442
 
6.2%
372
 
6.2%
232
 
6.2%
331
 
3.1%
Other values (5)5
15.6%
ValueCountFrequency (%)
232
6.2%
273
9.4%
284
12.5%
293
9.4%
303
9.4%
313
9.4%
324
12.5%
331
 
3.1%
341
 
3.1%
351
 
3.1%
ValueCountFrequency (%)
481
 
3.1%
461
 
3.1%
442
6.2%
372
6.2%
361
 
3.1%
351
 
3.1%
341
 
3.1%
331
 
3.1%
324
12.5%
313
9.4%

state_intervention
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.25
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:04.269648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5.5
Q39
95-th percentile22.8
Maximum25
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.900210375
Coefficient of variation (CV)0.8363891364
Kurtosis0.755369208
Mean8.25
Median Absolute Deviation (MAD)2.5
Skewness1.413726976
Sum264
Variance47.61290323
MonotonicityNot monotonic
2022-06-24T16:30:04.365670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
45
15.6%
54
12.5%
34
12.5%
23
9.4%
63
9.4%
73
9.4%
92
 
6.2%
252
 
6.2%
81
 
3.1%
131
 
3.1%
Other values (4)4
12.5%
ValueCountFrequency (%)
23
9.4%
34
12.5%
45
15.6%
54
12.5%
63
9.4%
73
9.4%
81
 
3.1%
92
 
6.2%
131
 
3.1%
181
 
3.1%
ValueCountFrequency (%)
252
6.2%
211
 
3.1%
201
 
3.1%
191
 
3.1%
181
 
3.1%
131
 
3.1%
92
6.2%
81
 
3.1%
73
9.4%
63
9.4%

conflict_between_states
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size384.0 B
2
12 
1
10 
0
9 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Length

2022-06-24T16:30:04.461691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T16:30:04.567715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring characters

ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.625
Minimum31
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:04.661736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile32
Q135.75
median40
Q349
95-th percentile54.45
Maximum56
Range25
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation7.732420228
Coefficient of variation (CV)0.1857638493
Kurtosis-1.076805083
Mean41.625
Median Absolute Deviation (MAD)7
Skewness0.4612043776
Sum1332
Variance59.79032258
MonotonicityNot monotonic
2022-06-24T16:30:04.764759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
405
15.6%
334
12.5%
383
 
9.4%
523
 
9.4%
492
 
6.2%
322
 
6.2%
411
 
3.1%
391
 
3.1%
481
 
3.1%
431
 
3.1%
Other values (9)9
28.1%
ValueCountFrequency (%)
311
 
3.1%
322
 
6.2%
334
12.5%
351
 
3.1%
361
 
3.1%
371
 
3.1%
383
9.4%
391
 
3.1%
405
15.6%
411
 
3.1%
ValueCountFrequency (%)
561
 
3.1%
551
 
3.1%
541
 
3.1%
523
9.4%
511
 
3.1%
492
6.2%
481
 
3.1%
431
 
3.1%
421
 
3.1%
411
 
3.1%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,896551692
 
1
5,255006083
 
1
1,055508247
 
1
1,086024514
 
1
2,680231114
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.15625
Min length11

Characters and Unicode

Total characters357
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,896551692
2nd row5,255006083
3rd row5,108261522
4th row1,923076559
5th row-0,976849818

Common Values

ValueCountFrequency (%)
3,8965516921
 
3.1%
5,2550060831
 
3.1%
1,0555082471
 
3.1%
1,0860245141
 
3.1%
2,6802311141
 
3.1%
2,2299998681
 
3.1%
1,4919315281
 
3.1%
2,2095434311
 
3.1%
0,4375913031
 
3.1%
0,4184975941
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:04.865782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,8965516921
 
3.1%
5,2550060831
 
3.1%
5,1082615221
 
3.1%
1,9230765591
 
3.1%
0,9768498181
 
3.1%
2,3918920711
 
3.1%
1,5441464961
 
3.1%
0,8058228911
 
3.1%
1,7921608211
 
3.1%
2,0139327851
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
151
14.3%
938
10.6%
833
9.2%
233
9.2%
,32
9.0%
531
8.7%
030
8.4%
628
7.8%
428
7.8%
326
7.3%
Other values (2)27
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number320
89.6%
Other Punctuation32
 
9.0%
Dash Punctuation5
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
151
15.9%
938
11.9%
833
10.3%
233
10.3%
531
9.7%
030
9.4%
628
8.8%
428
8.8%
326
8.1%
722
6.9%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common357
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
151
14.3%
938
10.6%
833
9.2%
233
9.2%
,32
9.0%
531
8.7%
030
8.4%
628
7.8%
428
7.8%
326
7.3%
Other values (2)27
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
14.3%
938
10.6%
833
9.2%
233
9.2%
,32
9.0%
531
8.7%
030
8.4%
628
7.8%
428
7.8%
326
7.3%
Other values (2)27
7.6%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
4,343861063
 
1
2,923935081
 
1
1,842971814
 
1
1,865066071
 
1
2,291419994
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.03125
Min length10

Characters and Unicode

Total characters353
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row4,343861063
2nd row2,923935081
3rd row1,048175847
4th row1,599342677
5th row-0,628666352

Common Values

ValueCountFrequency (%)
4,3438610631
 
3.1%
2,9239350811
 
3.1%
1,8429718141
 
3.1%
1,8650660711
 
3.1%
2,2914199941
 
3.1%
1,0954644041
 
3.1%
1,1129123411
 
3.1%
0,9561830521
 
3.1%
0,5763266751
 
3.1%
0,3131347511
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:04.967804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,3438610631
 
3.1%
2,9239350811
 
3.1%
1,0481758471
 
3.1%
1,5993426771
 
3.1%
0,6286663521
 
3.1%
2,3583421811
 
3.1%
2,1066952531
 
3.1%
1,4129936731
 
3.1%
2,3362965291
 
3.1%
3,5886594251
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
245
12.7%
343
12.2%
141
11.6%
935
9.9%
,32
9.1%
632
9.1%
430
8.5%
527
7.6%
723
6.5%
822
6.2%
Other values (2)23
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number318
90.1%
Other Punctuation32
 
9.1%
Dash Punctuation3
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
245
14.2%
343
13.5%
141
12.9%
935
11.0%
632
10.1%
430
9.4%
527
8.5%
723
7.2%
822
6.9%
020
6.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
245
12.7%
343
12.2%
141
11.6%
935
9.9%
,32
9.1%
632
9.1%
430
8.5%
527
7.6%
723
6.5%
822
6.2%
Other values (2)23
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
245
12.7%
343
12.2%
141
11.6%
935
9.9%
,32
9.1%
632
9.1%
430
8.5%
527
7.6%
723
6.5%
822
6.2%
Other values (2)23
6.5%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,388383454
 
1
1,985774943
 
1
0,410278294
 
1
0,925810941
 
1
1,667859041
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.125
Min length10

Characters and Unicode

Total characters356
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,388383454
2nd row1,985774943
3rd row1,538447558
4th row0,834275454
5th row-0,852805764

Common Values

ValueCountFrequency (%)
3,3883834541
 
3.1%
1,9857749431
 
3.1%
0,4102782941
 
3.1%
0,9258109411
 
3.1%
1,6678590411
 
3.1%
1,2934627321
 
3.1%
0,7783043511
 
3.1%
-0,0045475421
 
3.1%
-1,8410654511
 
3.1%
-2,9809057681
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:05.071828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,3883834541
 
3.1%
1,9857749431
 
3.1%
1,5384475581
 
3.1%
0,8342754541
 
3.1%
0,8528057641
 
3.1%
2,1510236381
 
3.1%
2,8868367591
 
3.1%
1,2667848021
 
3.1%
1,8302122341
 
3.1%
1,8106151621
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
840
11.2%
138
10.7%
534
9.6%
,32
9.0%
432
9.0%
232
9.0%
331
8.7%
031
8.7%
930
8.4%
728
7.9%
Other values (2)28
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number317
89.0%
Other Punctuation32
 
9.0%
Dash Punctuation7
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
840
12.6%
138
12.0%
534
10.7%
432
10.1%
232
10.1%
331
9.8%
031
9.8%
930
9.5%
728
8.8%
621
6.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
840
11.2%
138
10.7%
534
9.6%
,32
9.0%
432
9.0%
232
9.0%
331
8.7%
031
8.7%
930
8.4%
728
7.9%
Other values (2)28
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
840
11.2%
138
10.7%
534
9.6%
,32
9.0%
432
9.0%
232
9.0%
331
8.7%
031
8.7%
930
8.4%
728
7.9%
Other values (2)28
7.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
4,858037686
 
1
4,892713066
 
1
0,270304645
 
1
0,558851275
 
1
1,675331752
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.1875
Min length11

Characters and Unicode

Total characters358
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row4,858037686
2nd row4,892713066
3rd row3,417496762
4th row0,848069581
5th row-0,517919847

Common Values

ValueCountFrequency (%)
4,8580376861
 
3.1%
4,8927130661
 
3.1%
0,2703046451
 
3.1%
0,5588512751
 
3.1%
1,6753317521
 
3.1%
0,7538267461
 
3.1%
1,5606266971
 
3.1%
0,2962055141
 
3.1%
2,0051001771
 
3.1%
1,3747509991
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:05.171850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,8580376861
 
3.1%
4,8927130661
 
3.1%
3,4174967621
 
3.1%
0,8480695811
 
3.1%
0,5179198471
 
3.1%
0,9930663631
 
3.1%
2,6309996161
 
3.1%
3,1338709931
 
3.1%
0,9812287321
 
3.1%
1,2703304951
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
041
11.5%
939
10.9%
135
9.8%
333
9.2%
,32
8.9%
632
8.9%
232
8.9%
531
8.7%
826
7.3%
726
7.3%
Other values (2)31
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number320
89.4%
Other Punctuation32
 
8.9%
Dash Punctuation6
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
041
12.8%
939
12.2%
135
10.9%
333
10.3%
632
10.0%
232
10.0%
531
9.7%
826
8.1%
726
8.1%
425
7.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
041
11.5%
939
10.9%
135
9.8%
333
9.2%
,32
8.9%
632
8.9%
232
8.9%
531
8.7%
826
7.3%
726
7.3%
Other values (2)31
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
041
11.5%
939
10.9%
135
9.8%
333
9.2%
,32
8.9%
632
8.9%
232
8.9%
531
8.7%
826
7.3%
726
7.3%
Other values (2)31
8.7%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct23
Distinct (%)100.0%
Missing9
Missing (%)28.1%
Memory size384.0 B
2,79654248
 
1
1,879592028
 
1
2,777040554
 
1
3,039880225
 
1
1,001394414
 
1
Other values (18)
18 

Length

Max length12
Median length11
Mean length11
Min length10

Characters and Unicode

Total characters253
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row2,79654248
2nd row3,970482637
3rd row4,917762744
4th row1,405527589
5th row3,422146171

Common Values

ValueCountFrequency (%)
2,796542481
 
3.1%
1,8795920281
 
3.1%
2,7770405541
 
3.1%
3,0398802251
 
3.1%
1,0013944141
 
3.1%
0,6591768641
 
3.1%
2,8700360751
 
3.1%
2,3291225061
 
3.1%
1,7622225491
 
3.1%
3,1468813721
 
3.1%
Other values (13)13
40.6%
(Missing)9
28.1%

Length

2022-06-24T16:30:05.278874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,796542481
 
4.3%
1,0076226951
 
4.3%
3,9704826371
 
4.3%
4,9177627441
 
4.3%
1,4055275891
 
4.3%
3,4221461711
 
4.3%
3,8110901531
 
4.3%
3,9140287821
 
4.3%
4,9958608691
 
4.3%
4,1658176271
 
4.3%
Other values (13)13
56.5%

Most occurring characters

ValueCountFrequency (%)
232
12.6%
026
10.3%
424
9.5%
,23
9.1%
723
9.1%
823
9.1%
622
8.7%
122
8.7%
920
7.9%
518
7.1%
Other values (2)20
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number228
90.1%
Other Punctuation23
 
9.1%
Dash Punctuation2
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
232
14.0%
026
11.4%
424
10.5%
723
10.1%
823
10.1%
622
9.6%
122
9.6%
920
8.8%
518
7.9%
318
7.9%
Other Punctuation
ValueCountFrequency (%)
,23
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
232
12.6%
026
10.3%
424
9.5%
,23
9.1%
723
9.1%
823
9.1%
622
8.7%
122
8.7%
920
7.9%
518
7.1%
Other values (2)20
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
232
12.6%
026
10.3%
424
9.5%
,23
9.1%
723
9.1%
823
9.1%
622
8.7%
122
8.7%
920
7.9%
518
7.1%
Other values (2)20
7.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct31
Distinct (%)100.0%
Missing1
Missing (%)3.1%
Memory size384.0 B
-2,999995642
 
1
2,032982739
 
1
2,80724541
 
1
1,825790064
 
1
0,193690072
 
1
Other values (26)
26 

Length

Max length12
Median length11
Mean length11
Min length3

Characters and Unicode

Total characters341
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row-2,999995642
2nd row-5,046939451
3rd row-14,53107377
4th row-8,668540341
5th row-12,56975598

Common Values

ValueCountFrequency (%)
-2,9999956421
 
3.1%
2,0329827391
 
3.1%
2,807245411
 
3.1%
1,8257900641
 
3.1%
0,1936900721
 
3.1%
-1,9727192261
 
3.1%
0,7362672211
 
3.1%
1,7554221491
 
3.1%
4,0240861571
 
3.1%
4,3000291861
 
3.1%
Other values (21)21
65.6%

Length

2022-06-24T16:30:05.381897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,9999956421
 
3.2%
7,199947871
 
3.2%
5,0469394511
 
3.2%
14,531073771
 
3.2%
8,6685403411
 
3.2%
12,569755981
 
3.2%
4,1435284061
 
3.2%
3,7550694391
 
3.2%
1,3999158051
 
3.2%
5,2999616251
 
3.2%
Other values (21)21
67.7%

Most occurring characters

ValueCountFrequency (%)
960
17.6%
233
9.7%
532
9.4%
,31
9.1%
031
9.1%
128
8.2%
627
7.9%
426
7.6%
725
7.3%
319
 
5.6%
Other values (2)29
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number299
87.7%
Other Punctuation31
 
9.1%
Dash Punctuation11
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
960
20.1%
233
11.0%
532
10.7%
031
10.4%
128
9.4%
627
9.0%
426
8.7%
725
8.4%
319
 
6.4%
818
 
6.0%
Other Punctuation
ValueCountFrequency (%)
,31
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common341
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
960
17.6%
233
9.7%
532
9.4%
,31
9.1%
031
9.1%
128
8.2%
627
7.9%
426
7.6%
725
7.3%
319
 
5.6%
Other values (2)29
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
960
17.6%
233
9.7%
532
9.4%
,31
9.1%
031
9.1%
128
8.2%
627
7.9%
426
7.6%
725
7.3%
319
 
5.6%
Other values (2)29
8.5%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,672648159
 
1
1,885964958
 
1
2,161176515
 
1
2,996464352
 
1
2,332679395
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.0625
Min length10

Characters and Unicode

Total characters354
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,672648159
2nd row1,885964958
3rd row-0,108264787
4th row3,522440523
5th row2,751781042

Common Values

ValueCountFrequency (%)
3,6726481591
 
3.1%
1,8859649581
 
3.1%
2,1611765151
 
3.1%
2,9964643521
 
3.1%
2,3326793951
 
3.1%
1,7114267741
 
3.1%
3,075514651
 
3.1%
2,5259734461
 
3.1%
1,8420810711
 
3.1%
2,2495458521
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:05.612949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,6726481591
 
3.1%
1,8859649581
 
3.1%
0,1082647871
 
3.1%
3,5224405231
 
3.1%
2,7517810421
 
3.1%
4,0287932631
 
3.1%
2,6842172751
 
3.1%
3,7725655021
 
3.1%
4,3817751741
 
3.1%
4,4814075611
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
544
12.4%
739
11.0%
239
11.0%
138
10.7%
435
9.9%
,32
9.0%
328
7.9%
627
7.6%
825
7.1%
923
6.5%
Other values (2)24
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number318
89.8%
Other Punctuation32
 
9.0%
Dash Punctuation4
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
544
13.8%
739
12.3%
239
12.3%
138
11.9%
435
11.0%
328
8.8%
627
8.5%
825
7.9%
923
7.2%
020
6.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
544
12.4%
739
11.0%
239
11.0%
138
10.7%
435
9.9%
,32
9.0%
328
7.9%
627
7.6%
825
7.1%
923
6.5%
Other values (2)24
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
544
12.4%
739
11.0%
239
11.0%
138
10.7%
435
9.9%
,32
9.0%
328
7.9%
627
7.6%
825
7.1%
923
6.5%
Other values (2)24
6.8%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,577602692
 
1
0,73375552
 
1
1,671944197
 
1
1,650925496
 
1
2,134453093
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.03125
Min length10

Characters and Unicode

Total characters353
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,577602692
2nd row0,73375552
3rd row-1,103121662
4th row0,401082076
5th row2,489830985

Common Values

ValueCountFrequency (%)
2,5776026921
 
3.1%
0,733755521
 
3.1%
1,6719441971
 
3.1%
1,6509254961
 
3.1%
2,1344530931
 
3.1%
2,2634635381
 
3.1%
2,6225966791
 
3.1%
2,9911648141
 
3.1%
1,8900183421
 
3.1%
1,4698875211
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:05.716972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,5776026921
 
3.1%
0,733755521
 
3.1%
1,1031216621
 
3.1%
0,4010820761
 
3.1%
2,4898309851
 
3.1%
3,8460091681
 
3.1%
2,5316700051
 
3.1%
2,4285428951
 
3.1%
4,909025741
 
3.1%
3,1539733751
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
247
13.3%
635
9.9%
934
9.6%
133
9.3%
,32
9.1%
332
9.1%
031
8.8%
530
8.5%
426
7.4%
725
7.1%
Other values (2)28
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number317
89.8%
Other Punctuation32
 
9.1%
Dash Punctuation4
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
247
14.8%
635
11.0%
934
10.7%
133
10.4%
332
10.1%
031
9.8%
530
9.5%
426
8.2%
725
7.9%
824
7.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
247
13.3%
635
9.9%
934
9.6%
133
9.3%
,32
9.1%
332
9.1%
031
8.8%
530
8.5%
426
7.4%
725
7.1%
Other values (2)28
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
247
13.3%
635
9.9%
934
9.6%
133
9.3%
,32
9.1%
332
9.1%
031
8.8%
530
8.5%
426
7.4%
725
7.1%
Other values (2)28
7.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,16
 
1
-4,35
 
1
1,411152985
 
1
1,783666761
 
1
1,322869054
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length10.6875
Min length4

Characters and Unicode

Total characters342
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,16
2nd row-4,35
3rd row1,032189585
4th row-0,544072051
5th row4,924690005

Common Values

ValueCountFrequency (%)
3,161
 
3.1%
-4,351
 
3.1%
1,4111529851
 
3.1%
1,7836667611
 
3.1%
1,3228690541
 
3.1%
-3,2759169061
 
3.1%
-3,5457633931
 
3.1%
0,503955741
 
3.1%
3,004822671
 
3.1%
1,9211759851
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:05.821995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,161
 
3.1%
4,351
 
3.1%
1,0321895851
 
3.1%
0,5440720511
 
3.1%
4,9246900051
 
3.1%
5,8528703641
 
3.1%
4,2237936341
 
3.1%
2,2088640511
 
3.1%
3,3948459851
 
3.1%
0,3380979021
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
036
10.5%
935
10.2%
434
9.9%
333
9.6%
533
9.6%
,32
9.4%
829
8.5%
228
8.2%
126
7.6%
625
7.3%
Other values (2)31
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number304
88.9%
Other Punctuation32
 
9.4%
Dash Punctuation6
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036
11.8%
935
11.5%
434
11.2%
333
10.9%
533
10.9%
829
9.5%
228
9.2%
126
8.6%
625
8.2%
725
8.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036
10.5%
935
10.2%
434
9.9%
333
9.6%
533
9.6%
,32
9.4%
829
8.5%
228
8.2%
126
7.6%
625
7.3%
Other values (2)31
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036
10.5%
935
10.2%
434
9.9%
333
9.6%
533
9.6%
,32
9.4%
829
8.5%
228
8.2%
126
7.6%
625
7.3%
Other values (2)31
9.1%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
5,947343328
 
1
5,533454563
 
1
4,041554187
 
1
6,532989011
 
1
6,795383419
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters350
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row5,947343328
2nd row5,533454563
3rd row1,056831433
4th row5,482396022
5th row4,75077622

Common Values

ValueCountFrequency (%)
5,9473433281
 
3.1%
5,5334545631
 
3.1%
4,0415541871
 
3.1%
6,5329890111
 
3.1%
6,7953834191
 
3.1%
8,2563055021
 
3.1%
7,9962537861
 
3.1%
7,4102276051
 
3.1%
6,3861064011
 
3.1%
5,4563887531
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:05.926019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,9473433281
 
3.1%
5,5334545631
 
3.1%
1,0568314331
 
3.1%
5,4823960221
 
3.1%
4,750776221
 
3.1%
6,6589240671
 
3.1%
7,574491841
 
3.1%
7,5495222491
 
3.1%
4,0498208491
 
3.1%
6,1844158211
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
539
11.1%
836
10.3%
435
10.0%
635
10.0%
334
9.7%
,32
9.1%
731
8.9%
230
8.6%
027
7.7%
127
7.7%
Other values (2)24
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number317
90.6%
Other Punctuation32
 
9.1%
Dash Punctuation1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
539
12.3%
836
11.4%
435
11.0%
635
11.0%
334
10.7%
731
9.8%
230
9.5%
027
8.5%
127
8.5%
923
7.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
539
11.1%
836
10.3%
435
10.0%
635
10.0%
334
9.7%
,32
9.1%
731
8.9%
230
8.6%
027
7.7%
127
7.7%
Other values (2)24
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
539
11.1%
836
10.3%
435
10.0%
635
10.0%
334
9.7%
,32
9.1%
731
8.9%
230
8.6%
027
7.7%
127
7.7%
Other values (2)24
6.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
4,105509331
 
1
5,175768386
 
1
-0,176599456
 
1
2,194994725
 
1
2,113129135
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length11.125
Min length10

Characters and Unicode

Total characters356
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row4,105509331
2nd row5,175768386
3rd row4,214754839
4th row3,541102416
5th row1,941155848

Common Values

ValueCountFrequency (%)
4,1055093311
 
3.1%
5,1757683861
 
3.1%
-0,1765994561
 
3.1%
2,1949947251
 
3.1%
2,1131291351
 
3.1%
2,6305324251
 
3.1%
3,2931515281
 
3.1%
2,8497732551
 
3.1%
1,3540919621
 
3.1%
3,6423226791
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.030042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,1055093311
 
3.1%
5,1757683861
 
3.1%
4,2147548391
 
3.1%
3,5411024161
 
3.1%
1,9411558481
 
3.1%
4,9410806761
 
3.1%
6,2912308211
 
3.1%
6,7732586941
 
3.1%
6,8468522791
 
3.1%
5,1639251681
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
444
12.4%
140
11.2%
537
10.4%
234
9.6%
333
9.3%
,32
9.0%
930
8.4%
826
7.3%
725
7.0%
625
7.0%
Other values (2)30
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number318
89.3%
Other Punctuation32
 
9.0%
Dash Punctuation6
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
444
13.8%
140
12.6%
537
11.6%
234
10.7%
333
10.4%
930
9.4%
826
8.2%
725
7.9%
625
7.9%
024
7.5%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
444
12.4%
140
11.2%
537
10.4%
234
9.6%
333
9.3%
,32
9.0%
930
8.4%
826
7.3%
725
7.0%
625
7.0%
Other values (2)30
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
444
12.4%
140
11.2%
537
10.4%
234
9.6%
333
9.3%
,32
9.0%
930
8.4%
826
7.3%
725
7.0%
625
7.0%
Other values (2)30
8.4%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,394784159
 
1
-0,317785676
 
1
0,113053697
 
1
1,487617373
 
1
1,157946952
 
1
Other values (27)
27 

Length

Max length12
Median length11
Mean length10.65625
Min length3

Characters and Unicode

Total characters341
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,394784159
2nd row-0,317785676
3rd row-1,018219873
4th row-2,137056889
5th row1,233519913

Common Values

ValueCountFrequency (%)
2,3947841591
 
3.1%
-0,3177856761
 
3.1%
0,1130536971
 
3.1%
1,4876173731
 
3.1%
1,1579469521
 
3.1%
0,6645523081
 
3.1%
1,3218622371
 
3.1%
1,4138264521
 
3.1%
2,4854680081
 
3.1%
2,3962323851
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.130064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,3947841591
 
3.1%
0,3177856761
 
3.1%
1,0182198731
 
3.1%
2,1370568891
 
3.1%
1,2335199131
 
3.1%
3,2000000031
 
3.1%
3,1000000011
 
3.1%
4,2999999971
 
3.1%
2,6000000011
 
3.1%
0,51
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
051
15.0%
336
10.6%
935
10.3%
,32
9.4%
131
9.1%
528
8.2%
226
7.6%
726
7.6%
425
7.3%
824
7.0%
Other values (2)27
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number304
89.1%
Other Punctuation32
 
9.4%
Dash Punctuation5
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
051
16.8%
336
11.8%
935
11.5%
131
10.2%
528
9.2%
226
8.6%
726
8.6%
425
8.2%
824
7.9%
622
7.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common341
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
051
15.0%
336
10.6%
935
10.3%
,32
9.4%
131
9.1%
528
8.2%
226
7.6%
726
7.6%
425
7.3%
824
7.0%
Other values (2)27
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
051
15.0%
336
10.6%
935
10.3%
,32
9.4%
131
9.1%
528
8.2%
226
7.6%
726
7.6%
425
7.3%
824
7.0%
Other values (2)27
7.9%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
4,206334355
 
1
3,920251368
 
1
5,949714233
 
1
6,749773832
 
1
6,947200793
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length10

Characters and Unicode

Total characters349
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row4,206334355
2nd row3,920251368
3rd row9,262786084
4th row14,22452959
5th row13,8837293

Common Values

ValueCountFrequency (%)
4,2063343551
 
3.1%
3,9202513681
 
3.1%
5,9497142331
 
3.1%
6,7497738321
 
3.1%
6,9472007931
 
3.1%
6,8487622051
 
3.1%
7,0413288791
 
3.1%
7,4257636561
 
3.1%
7,7661500981
 
3.1%
7,8637364491
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.230087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,2063343551
 
3.1%
3,9202513681
 
3.1%
9,2627860841
 
3.1%
14,224529591
 
3.1%
13,88372931
 
3.1%
13,036806631
 
3.1%
10,953954341
 
3.1%
9,9225567521
 
3.1%
9,2367798921
 
3.1%
7,8459517881
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
345
12.9%
935
10.0%
734
9.7%
633
9.5%
,32
9.2%
230
8.6%
530
8.6%
029
8.3%
829
8.3%
128
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number317
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
345
14.2%
935
11.0%
734
10.7%
633
10.4%
230
9.5%
530
9.5%
029
9.1%
829
9.1%
128
8.8%
424
7.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common349
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
345
12.9%
935
10.0%
734
9.7%
633
9.5%
,32
9.2%
230
8.6%
530
8.6%
029
8.3%
829
8.3%
128
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
345
12.9%
935
10.0%
734
9.7%
633
9.5%
,32
9.2%
230
8.6%
530
8.6%
029
8.3%
829
8.3%
128
8.0%

GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,74060573
 
1
2,876245079
 
1
2,600814244
 
1
3,269583641
 
1
3,394374337
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.8125
Min length9

Characters and Unicode

Total characters346
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,74060573
2nd row2,876245079
3rd row1,446499134
4th row2,056125611
5th row1,808166661

Common Values

ValueCountFrequency (%)
3,740605731
 
3.1%
2,8762450791
 
3.1%
2,6008142441
 
3.1%
3,2695836411
 
3.1%
3,3943743371
 
3.1%
2,8250572881
 
3.1%
3,1683024481
 
3.1%
3,1177528171
 
3.1%
2,8446516331
 
3.1%
2,6728184111
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.332109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,740605731
 
3.1%
2,8762450791
 
3.1%
1,4464991341
 
3.1%
2,0561256111
 
3.1%
1,8081666611
 
3.1%
3,2968990351
 
3.1%
3,0893539021
 
3.1%
3,613661861
 
3.1%
3,8690314931
 
3.1%
2,7931740311
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
348
13.9%
440
11.6%
634
9.8%
133
9.5%
,32
9.2%
231
9.0%
030
8.7%
827
7.8%
723
6.6%
523
6.6%
Other values (2)25
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312
90.2%
Other Punctuation32
 
9.2%
Dash Punctuation2
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
348
15.4%
440
12.8%
634
10.9%
133
10.6%
231
9.9%
030
9.6%
827
8.7%
723
7.4%
523
7.4%
923
7.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
348
13.9%
440
11.6%
634
9.8%
133
9.5%
,32
9.2%
231
9.0%
030
8.7%
827
7.8%
723
6.6%
523
6.6%
Other values (2)25
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
348
13.9%
440
11.6%
634
9.8%
133
9.5%
,32
9.2%
231
9.0%
030
8.7%
827
7.8%
723
6.6%
523
6.6%
Other values (2)25
7.2%

GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
1,39897E+12
 
1
1,77167E+12
 
1
3,88833E+12
 
1
3,97729E+12
 
1
3,69085E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters350
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row1,39897E+12
2nd row1,77167E+12
3rd row1,86895E+12
4th row2,13157E+12
5th row2,07132E+12

Common Values

ValueCountFrequency (%)
1,39897E+121
 
3.1%
1,77167E+121
 
3.1%
3,88833E+121
 
3.1%
3,97729E+121
 
3.1%
3,69085E+121
 
3.1%
3,46985E+121
 
3.1%
3,35759E+121
 
3.1%
3,88909E+121
 
3.1%
3,7338E+121
 
3.1%
3,52714E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.433132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,39897e+121
 
3.1%
1,77167e+121
 
3.1%
1,86895e+121
 
3.1%
2,13157e+121
 
3.1%
2,07132e+121
 
3.1%
2,20507e+121
 
3.1%
2,58579e+121
 
3.1%
2,49724e+121
 
3.1%
2,21199e+121
 
3.1%
2,23899e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
255
15.7%
151
14.6%
,32
9.1%
E32
9.1%
+32
9.1%
927
7.7%
326
7.4%
821
 
6.0%
721
 
6.0%
418
 
5.1%
Other values (3)35
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number254
72.6%
Other Punctuation32
 
9.1%
Uppercase Letter32
 
9.1%
Math Symbol32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
255
21.7%
151
20.1%
927
10.6%
326
10.2%
821
 
8.3%
721
 
8.3%
418
 
7.1%
517
 
6.7%
611
 
4.3%
07
 
2.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common318
90.9%
Latin32
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
255
17.3%
151
16.0%
,32
10.1%
+32
10.1%
927
8.5%
326
8.2%
821
 
6.6%
721
 
6.6%
418
 
5.7%
517
 
5.3%
Other values (2)18
 
5.7%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
255
15.7%
151
14.6%
,32
9.1%
E32
9.1%
+32
9.1%
927
7.7%
326
7.4%
821
 
6.0%
721
 
6.0%
418
 
5.1%
Other values (3)35
10.0%

GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
1,02521E+12
 
1
1,26918E+12
 
1
2,72887E+12
 
1
2,79096E+12
 
1
2,59515E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row1,02521E+12
2nd row1,26918E+12
3rd row1,26928E+12
4th row1,40147E+12
5th row1,32282E+12

Common Values

ValueCountFrequency (%)
1,02521E+121
 
3.1%
1,26918E+121
 
3.1%
2,72887E+121
 
3.1%
2,79096E+121
 
3.1%
2,59515E+121
 
3.1%
2,47296E+121
 
3.1%
2,43919E+121
 
3.1%
2,85596E+121
 
3.1%
2,81188E+121
 
3.1%
2,68367E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.533154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,02521e+121
 
3.1%
1,26918e+121
 
3.1%
1,26928e+121
 
3.1%
1,40147e+121
 
3.1%
1,32282e+121
 
3.1%
1,39398e+121
 
3.1%
1,60109e+121
 
3.1%
1,60568e+121
 
3.1%
1,45288e+121
 
3.1%
1,50311e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
165
18.5%
262
17.7%
,32
9.1%
E32
9.1%
+32
9.1%
622
 
6.3%
920
 
5.7%
518
 
5.1%
817
 
4.8%
315
 
4.3%
Other values (3)36
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number255
72.6%
Other Punctuation32
 
9.1%
Uppercase Letter32
 
9.1%
Math Symbol32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
165
25.5%
262
24.3%
622
 
8.6%
920
 
7.8%
518
 
7.1%
817
 
6.7%
315
 
5.9%
014
 
5.5%
413
 
5.1%
79
 
3.5%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common319
90.9%
Latin32
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
165
20.4%
262
19.4%
,32
10.0%
+32
10.0%
622
 
6.9%
920
 
6.3%
518
 
5.6%
817
 
5.3%
315
 
4.7%
014
 
4.4%
Other values (2)22
 
6.9%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
18.5%
262
17.7%
,32
9.1%
E32
9.1%
+32
9.1%
622
 
6.3%
920
 
5.7%
518
 
5.1%
817
 
4.8%
315
 
4.3%
Other values (3)36
10.3%

GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
9,28661E+11
 
1
1,18122E+12
 
1
2,00938E+12
 
1
2,09193E+12
 
1
1,9618E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length10

Characters and Unicode

Total characters349
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row9,28661E+11
2nd row1,18122E+12
3rd row1,24622E+12
4th row1,32016E+12
5th row1,06496E+12

Common Values

ValueCountFrequency (%)
9,28661E+111
 
3.1%
1,18122E+121
 
3.1%
2,00938E+121
 
3.1%
2,09193E+121
 
3.1%
1,9618E+121
 
3.1%
1,87707E+121
 
3.1%
1,83664E+121
 
3.1%
2,16201E+121
 
3.1%
2,14192E+121
 
3.1%
2,08696E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.633177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,28661e+111
 
3.1%
1,18122e+121
 
3.1%
1,24622e+121
 
3.1%
1,32016e+121
 
3.1%
1,06496e+121
 
3.1%
1,09922e+121
 
3.1%
1,17466e+121
 
3.1%
1,31243e+121
 
3.1%
1,24188e+121
 
3.1%
1,27005e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
175
21.5%
264
18.3%
,32
9.2%
E32
9.2%
+32
9.2%
623
 
6.6%
918
 
5.2%
818
 
5.2%
014
 
4.0%
413
 
3.7%
Other values (3)28
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
72.5%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
175
29.6%
264
25.3%
623
 
9.1%
918
 
7.1%
818
 
7.1%
014
 
5.5%
413
 
5.1%
711
 
4.3%
39
 
3.6%
58
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common317
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
175
23.7%
264
20.2%
,32
10.1%
+32
10.1%
623
 
7.3%
918
 
5.7%
818
 
5.7%
014
 
4.4%
413
 
4.1%
711
 
3.5%
Other values (2)17
 
5.4%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
21.5%
264
18.3%
,32
9.2%
E32
9.2%
+32
9.2%
623
 
6.6%
918
 
5.2%
818
 
5.2%
014
 
4.0%
413
 
3.7%
Other values (3)28
 
8.0%

GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,05491E+12
 
1
3,13282E+12
 
1
5,14878E+12
 
1
5,03689E+12
 
1
4,93084E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,05491E+12
2nd row3,13282E+12
3rd row3,58442E+12
4th row3,90881E+12
5th row4,45414E+12

Common Values

ValueCountFrequency (%)
3,05491E+121
 
3.1%
3,13282E+121
 
3.1%
5,14878E+121
 
3.1%
5,03689E+121
 
3.1%
4,93084E+121
 
3.1%
5,00368E+121
 
3.1%
4,44493E+121
 
3.1%
4,89699E+121
 
3.1%
5,21233E+121
 
3.1%
6,27236E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.733199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,05491e+121
 
3.1%
3,13282e+121
 
3.1%
3,58442e+121
 
3.1%
3,90881e+121
 
3.1%
4,45414e+121
 
3.1%
4,9988e+121
 
3.1%
5,54556e+121
 
3.1%
4,92339e+121
 
3.1%
4,49245e+121
 
3.1%
4,09836e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
146
13.1%
245
12.8%
434
9.7%
,32
9.1%
E32
9.1%
+32
9.1%
525
7.1%
322
6.3%
922
6.3%
821
6.0%
Other values (3)40
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number255
72.6%
Other Punctuation32
 
9.1%
Uppercase Letter32
 
9.1%
Math Symbol32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
146
18.0%
245
17.6%
434
13.3%
525
9.8%
322
8.6%
922
8.6%
821
8.2%
618
 
7.1%
011
 
4.3%
711
 
4.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common319
90.9%
Latin32
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
146
14.4%
245
14.1%
434
10.7%
,32
10.0%
+32
10.0%
525
7.8%
322
6.9%
922
6.9%
821
6.6%
618
 
5.6%
Other values (2)22
6.9%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
146
13.1%
245
12.8%
434
9.7%
,32
9.1%
E32
9.1%
+32
9.1%
525
7.1%
322
6.3%
922
6.3%
821
6.0%
Other values (3)40
11.4%

GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
5,65056E+11
 
1
5,9393E+11
 
1
1,74202E+12
 
1
1,72533E+12
 
1
1,64927E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length8

Characters and Unicode

Total characters347
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row5,65056E+11
2nd row5,9393E+11
3rd row6,10328E+11
4th row5,92388E+11
5th row5,77171E+11

Common Values

ValueCountFrequency (%)
5,65056E+111
 
3.1%
5,9393E+111
 
3.1%
1,74202E+121
 
3.1%
1,72533E+121
 
3.1%
1,64927E+121
 
3.1%
1,52799E+121
 
3.1%
1,55651E+121
 
3.1%
1,80575E+121
 
3.1%
1,8466E+121
 
3.1%
1,82837E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.836222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,65056e+111
 
3.1%
5,9393e+111
 
3.1%
6,10328e+111
 
3.1%
5,92388e+111
 
3.1%
5,77171e+111
 
3.1%
5,78139e+111
 
3.1%
6,04032e+111
 
3.1%
6,28546e+111
 
3.1%
6,54987e+111
 
3.1%
6,34e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
176
21.9%
234
9.8%
,32
9.2%
E32
9.2%
+32
9.2%
622
 
6.3%
521
 
6.1%
721
 
6.1%
320
 
5.8%
417
 
4.9%
Other values (3)40
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251
72.3%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
176
30.3%
234
13.5%
622
 
8.8%
521
 
8.4%
721
 
8.4%
320
 
8.0%
417
 
6.8%
916
 
6.4%
816
 
6.4%
08
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common315
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
176
24.1%
234
10.8%
,32
10.2%
+32
10.2%
622
 
7.0%
521
 
6.7%
721
 
6.7%
320
 
6.3%
417
 
5.4%
916
 
5.1%
Other values (2)24
 
7.6%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
176
21.9%
234
9.8%
,32
9.2%
E32
9.2%
+32
9.2%
622
 
6.3%
521
 
6.1%
721
 
6.1%
320
 
5.8%
417
 
4.9%
Other values (3)40
11.5%

GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
5,065E+11
 
1
5,16814E+11
 
1
1,68745E+12
 
1
1,65733E+12
 
1
1,5742E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.78125
Min length9

Characters and Unicode

Total characters345
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row5,065E+11
2nd row5,16814E+11
3rd row5,17963E+11
4th row4,60291E+11
5th row4,35084E+11

Common Values

ValueCountFrequency (%)
5,065E+111
 
3.1%
5,16814E+111
 
3.1%
1,68745E+121
 
3.1%
1,65733E+121
 
3.1%
1,5742E+121
 
3.1%
1,27679E+121
 
3.1%
1,36348E+121
 
3.1%
2,05924E+121
 
3.1%
2,29247E+121
 
3.1%
2,2083E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:06.939245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,065e+111
 
3.1%
5,16814e+111
 
3.1%
5,17963e+111
 
3.1%
4,60291e+111
 
3.1%
4,35084e+111
 
3.1%
3,95077e+111
 
3.1%
3,95537e+111
 
3.1%
3,91725e+111
 
3.1%
4,04929e+111
 
3.1%
2,70955e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
172
20.9%
236
10.4%
,32
9.3%
E32
9.3%
+32
9.3%
523
 
6.7%
922
 
6.4%
420
 
5.8%
719
 
5.5%
318
 
5.2%
Other values (3)39
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number249
72.2%
Other Punctuation32
 
9.3%
Uppercase Letter32
 
9.3%
Math Symbol32
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
172
28.9%
236
14.5%
523
 
9.2%
922
 
8.8%
420
 
8.0%
719
 
7.6%
318
 
7.2%
016
 
6.4%
614
 
5.6%
89
 
3.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common313
90.7%
Latin32
 
9.3%

Most frequent character per script

Common
ValueCountFrequency (%)
172
23.0%
236
11.5%
,32
10.2%
+32
10.2%
523
 
7.3%
922
 
7.0%
420
 
6.4%
719
 
6.1%
318
 
5.8%
016
 
5.1%
Other values (2)23
 
7.3%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
20.9%
236
10.4%
,32
9.3%
E32
9.3%
+32
9.3%
523
 
6.7%
922
 
6.4%
420
 
5.8%
719
 
5.5%
318
 
5.2%
Other values (3)39
11.3%

GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
5,64158E+12
 
1
5,96314E+12
 
1
2,14332E+13
 
1
2,06119E+13
 
1
1,9543E+13
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length10

Characters and Unicode

Total characters349
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row5,64158E+12
2nd row5,96314E+12
3rd row6,15813E+12
4th row6,52033E+12
5th row6,85856E+12

Common Values

ValueCountFrequency (%)
5,64158E+121
 
3.1%
5,96314E+121
 
3.1%
2,14332E+131
 
3.1%
2,06119E+131
 
3.1%
1,9543E+131
 
3.1%
1,87451E+131
 
3.1%
1,82383E+131
 
3.1%
1,75272E+131
 
3.1%
1,67848E+131
 
3.1%
1,6197E+131
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.042269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,64158e+121
 
3.1%
5,96314e+121
 
3.1%
6,15813e+121
 
3.1%
6,52033e+121
 
3.1%
6,85856e+121
 
3.1%
7,28724e+121
 
3.1%
7,63975e+121
 
3.1%
8,07312e+121
 
3.1%
8,57755e+121
 
3.1%
9,06282e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
167
19.2%
340
11.5%
,32
9.2%
E32
9.2%
+32
9.2%
232
9.2%
521
 
6.0%
619
 
5.4%
819
 
5.4%
418
 
5.2%
Other values (3)37
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
72.5%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
167
26.5%
340
15.8%
232
12.6%
521
 
8.3%
619
 
7.5%
819
 
7.5%
418
 
7.1%
714
 
5.5%
913
 
5.1%
010
 
4.0%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common317
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
167
21.1%
340
12.6%
,32
10.1%
+32
10.1%
232
10.1%
521
 
6.6%
619
 
6.0%
819
 
6.0%
418
 
5.7%
714
 
4.4%
Other values (2)23
 
7.3%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
167
19.2%
340
11.5%
,32
9.2%
E32
9.2%
+32
9.2%
232
9.2%
521
 
6.0%
619
 
5.4%
819
 
5.4%
418
 
5.2%
Other values (3)37
10.6%

GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
9,26885E+11
 
1
1,09317E+12
 
1
2,87867E+12
 
1
2,90079E+12
 
1
2,69902E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length10

Characters and Unicode

Total characters349
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row9,26885E+11
2nd row1,09317E+12
3rd row1,1428E+12
4th row1,17966E+12
5th row1,06139E+12

Common Values

ValueCountFrequency (%)
9,26885E+111
 
3.1%
1,09317E+121
 
3.1%
2,87867E+121
 
3.1%
2,90079E+121
 
3.1%
2,69902E+121
 
3.1%
2,72285E+121
 
3.1%
2,95657E+121
 
3.1%
3,08717E+121
 
3.1%
2,80329E+121
 
3.1%
2,71916E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.143291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,26885e+111
 
3.1%
1,09317e+121
 
3.1%
1,1428e+121
 
3.1%
1,17966e+121
 
3.1%
1,06139e+121
 
3.1%
1,14049e+121
 
3.1%
1,34642e+121
 
3.1%
1,42151e+121
 
3.1%
1,55957e+121
 
3.1%
1,65339e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
166
18.9%
258
16.6%
,32
9.2%
E32
9.2%
+32
9.2%
920
 
5.7%
820
 
5.7%
618
 
5.2%
718
 
5.2%
514
 
4.0%
Other values (3)39
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
72.5%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
166
26.1%
258
22.9%
920
 
7.9%
820
 
7.9%
618
 
7.1%
718
 
7.1%
514
 
5.5%
414
 
5.5%
013
 
5.1%
312
 
4.7%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common317
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
166
20.8%
258
18.3%
,32
10.1%
+32
10.1%
920
 
6.3%
820
 
6.3%
618
 
5.7%
718
 
5.7%
514
 
4.4%
414
 
4.4%
Other values (2)25
 
7.9%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
166
18.9%
258
16.6%
,32
9.2%
E32
9.2%
+32
9.2%
920
 
5.7%
820
 
5.7%
618
 
5.2%
718
 
5.2%
514
 
4.0%
Other values (3)39
11.2%

GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,47028E+11
 
1
3,90726E+11
 
1
1,87782E+12
 
1
1,91693E+12
 
1
2,06351E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length9

Characters and Unicode

Total characters349
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,47028E+11
2nd row3,90726E+11
3rd row3,42609E+11
4th row3,28188E+11
5th row3,68296E+11

Common Values

ValueCountFrequency (%)
3,47028E+111
 
3.1%
3,90726E+111
 
3.1%
1,87782E+121
 
3.1%
1,91693E+121
 
3.1%
2,06351E+121
 
3.1%
1,79569E+121
 
3.1%
1,80221E+121
 
3.1%
2,45604E+121
 
3.1%
2,47282E+121
 
3.1%
2,46523E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.249315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,47028e+111
 
3.1%
3,90726e+111
 
3.1%
3,42609e+111
 
3.1%
3,28188e+111
 
3.1%
3,68296e+111
 
3.1%
5,2537e+111
 
3.1%
7,69333e+111
 
3.1%
8,50426e+111
 
3.1%
8,83206e+111
 
3.1%
8,63711e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
170
20.1%
239
11.2%
,32
9.2%
E32
9.2%
+32
9.2%
626
 
7.4%
821
 
6.0%
319
 
5.4%
919
 
5.4%
417
 
4.9%
Other values (3)42
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
72.5%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
170
27.7%
239
15.4%
626
 
10.3%
821
 
8.3%
319
 
7.5%
919
 
7.5%
417
 
6.7%
517
 
6.7%
714
 
5.5%
011
 
4.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common317
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
170
22.1%
239
12.3%
,32
10.1%
+32
10.1%
626
 
8.2%
821
 
6.6%
319
 
6.0%
919
 
6.0%
417
 
5.4%
517
 
5.4%
Other values (2)25
 
7.9%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
170
20.1%
239
11.2%
,32
9.2%
E32
9.2%
+32
9.2%
626
 
7.4%
821
 
6.0%
319
 
5.4%
919
 
5.4%
417
 
4.9%
Other values (3)42
12.0%

GDP (current US$) [NY.GDP.MKTP.CD] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,96042E+11
 
1
3,20979E+11
 
1
2,8705E+12
 
1
2,70111E+12
 
1
2,65147E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length10

Characters and Unicode

Total characters347
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,96042E+11
2nd row3,20979E+11
3rd row2,70105E+11
4th row2,88208E+11
5th row2,79296E+11

Common Values

ValueCountFrequency (%)
2,96042E+111
 
3.1%
3,20979E+111
 
3.1%
2,8705E+121
 
3.1%
2,70111E+121
 
3.1%
2,65147E+121
 
3.1%
2,2948E+121
 
3.1%
2,10359E+121
 
3.1%
2,03913E+121
 
3.1%
1,85672E+121
 
3.1%
1,82764E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.352338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,96042e+111
 
3.1%
3,20979e+111
 
3.1%
2,70105e+111
 
3.1%
2,88208e+111
 
3.1%
2,79296e+111
 
3.1%
3,27276e+111
 
3.1%
3,60282e+111
 
3.1%
3,92897e+111
 
3.1%
4,15868e+111
 
3.1%
4,21351e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
173
21.0%
246
13.3%
,32
9.2%
E32
9.2%
+32
9.2%
821
 
6.1%
920
 
5.8%
617
 
4.9%
016
 
4.6%
416
 
4.6%
Other values (3)42
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251
72.3%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
173
29.1%
246
18.3%
821
 
8.4%
920
 
8.0%
617
 
6.8%
016
 
6.4%
416
 
6.4%
715
 
6.0%
514
 
5.6%
313
 
5.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common315
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
173
23.2%
246
14.6%
,32
10.2%
+32
10.2%
821
 
6.7%
920
 
6.3%
617
 
5.4%
016
 
5.1%
416
 
5.1%
715
 
4.8%
Other values (2)27
 
8.6%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
173
21.0%
246
13.3%
,32
9.2%
E32
9.2%
+32
9.2%
821
 
6.1%
920
 
5.8%
617
 
4.9%
016
 
4.6%
416
 
4.6%
Other values (3)42
12.1%

GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,21401E+11
 
1
2,61254E+11
 
1
1,26943E+12
 
1
1,22241E+12
 
1
1,15891E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters350
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,21401E+11
2nd row2,61254E+11
3rd row3,13143E+11
4th row3,63158E+11
5th row5,00736E+11

Common Values

ValueCountFrequency (%)
2,21401E+111
 
3.1%
2,61254E+111
 
3.1%
1,26943E+121
 
3.1%
1,22241E+121
 
3.1%
1,15891E+121
 
3.1%
1,07849E+121
 
3.1%
1,17187E+121
 
3.1%
1,31535E+121
 
3.1%
1,27444E+121
 
3.1%
1,20109E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.450360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,21401e+111
 
3.1%
2,61254e+111
 
3.1%
3,13143e+111
 
3.1%
3,63158e+111
 
3.1%
5,00736e+111
 
3.1%
5,27813e+111
 
3.1%
3,60074e+111
 
3.1%
4,10976e+111
 
3.1%
5,00413e+111
 
3.1%
5,26502e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
184
24.0%
233
 
9.4%
,32
 
9.1%
E32
 
9.1%
+32
 
9.1%
026
 
7.4%
725
 
7.1%
318
 
5.1%
416
 
4.6%
515
 
4.3%
Other values (3)37
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number254
72.6%
Other Punctuation32
 
9.1%
Uppercase Letter32
 
9.1%
Math Symbol32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
184
33.1%
233
 
13.0%
026
 
10.2%
725
 
9.8%
318
 
7.1%
416
 
6.3%
515
 
5.9%
914
 
5.5%
613
 
5.1%
810
 
3.9%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common318
90.9%
Latin32
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
184
26.4%
233
 
10.4%
,32
 
10.1%
+32
 
10.1%
026
 
8.2%
725
 
7.9%
318
 
5.7%
416
 
5.0%
515
 
4.7%
914
 
4.4%
Other values (2)23
 
7.2%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
24.0%
233
 
9.4%
,32
 
9.1%
E32
 
9.1%
+32
 
9.1%
026
 
7.4%
725
 
7.1%
318
 
5.1%
416
 
4.6%
515
 
4.3%
Other values (3)37
10.6%

GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
99030856825
 
1
1,15552E+11
 
1
3,87935E+11
 
1
4,04842E+11
 
1
3,81449E+11
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length10

Characters and Unicode

Total characters349
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row99030856825
2nd row1,15552E+11
3rd row1,23943E+11
4th row1,34545E+11
5th row1,47197E+11

Common Values

ValueCountFrequency (%)
990308568251
 
3.1%
1,15552E+111
 
3.1%
3,87935E+111
 
3.1%
4,04842E+111
 
3.1%
3,81449E+111
 
3.1%
3,23586E+111
 
3.1%
3,4671E+111
 
3.1%
3,81199E+111
 
3.1%
4,00886E+111
 
3.1%
4,34401E+111
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.551382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
990308568251
 
3.1%
1,15552e+111
 
3.1%
1,23943e+111
 
3.1%
1,34545e+111
 
3.1%
1,47197e+111
 
3.1%
1,53513e+111
 
3.1%
1,71735e+111
 
3.1%
1,63237e+111
 
3.1%
1,68977e+111
 
3.1%
1,52983e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
192
26.4%
333
 
9.5%
,31
 
8.9%
E31
 
8.9%
+31
 
8.9%
525
 
7.2%
820
 
5.7%
419
 
5.4%
216
 
4.6%
716
 
4.6%
Other values (3)35
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number256
73.4%
Other Punctuation31
 
8.9%
Uppercase Letter31
 
8.9%
Math Symbol31
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
192
35.9%
333
 
12.9%
525
 
9.8%
820
 
7.8%
419
 
7.4%
216
 
6.2%
716
 
6.2%
913
 
5.1%
012
 
4.7%
610
 
3.9%
Other Punctuation
ValueCountFrequency (%)
,31
100.0%
Uppercase Letter
ValueCountFrequency (%)
E31
100.0%
Math Symbol
ValueCountFrequency (%)
+31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common318
91.1%
Latin31
 
8.9%

Most frequent character per script

Common
ValueCountFrequency (%)
192
28.9%
333
 
10.4%
,31
 
9.7%
+31
 
9.7%
525
 
7.9%
820
 
6.3%
419
 
6.0%
216
 
5.0%
716
 
5.0%
913
 
4.1%
Other values (2)22
 
6.9%
Latin
ValueCountFrequency (%)
E31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
26.4%
333
 
9.5%
,31
 
8.9%
E31
 
8.9%
+31
 
8.9%
525
 
7.2%
820
 
5.7%
419
 
5.4%
216
 
4.6%
716
 
4.6%
Other values (3)35
 
10.0%

GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,47768E+11
 
1
3,60858E+11
 
1
1,42799E+13
 
1
1,38948E+13
 
1
1,23104E+13
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length9

Characters and Unicode

Total characters347
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,47768E+11
2nd row3,60858E+11
3rd row3,83373E+11
4th row4,26916E+11
5th row4,44731E+11

Common Values

ValueCountFrequency (%)
3,47768E+111
 
3.1%
3,60858E+111
 
3.1%
1,42799E+131
 
3.1%
1,38948E+131
 
3.1%
1,23104E+131
 
3.1%
1,12333E+131
 
3.1%
1,10616E+131
 
3.1%
1,04757E+131
 
3.1%
9,57041E+121
 
3.1%
8,53223E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.653405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,47768e+111
 
3.1%
3,60858e+111
 
3.1%
3,83373e+111
 
3.1%
4,26916e+111
 
3.1%
4,44731e+111
 
3.1%
5,64325e+111
 
3.1%
7,34548e+111
 
3.1%
8,63747e+111
 
3.1%
9,61604e+111
 
3.1%
1,02904e+121
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
171
20.5%
332
9.2%
,32
9.2%
E32
9.2%
+32
9.2%
232
9.2%
423
 
6.6%
522
 
6.3%
719
 
5.5%
614
 
4.0%
Other values (3)38
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251
72.3%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
171
28.3%
332
12.7%
232
12.7%
423
 
9.2%
522
 
8.8%
719
 
7.6%
614
 
5.6%
014
 
5.6%
913
 
5.2%
811
 
4.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common315
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
171
22.5%
332
10.2%
,32
10.2%
+32
10.2%
232
10.2%
423
 
7.3%
522
 
7.0%
719
 
6.0%
614
 
4.4%
014
 
4.4%
Other values (2)24
 
7.6%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
20.5%
332
9.2%
,32
9.2%
E32
9.2%
+32
9.2%
232
9.2%
423
 
6.6%
522
 
6.3%
719
 
5.5%
614
 
4.0%
Other values (3)38
11.0%

GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,01713E+13
 
1
2,27395E+13
 
1
8,75681E+13
 
1
8,62743E+13
 
1
8,12246E+13
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters348
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,01713E+13
2nd row2,27395E+13
3rd row2,3707E+13
4th row2,53937E+13
5th row2,58225E+13

Common Values

ValueCountFrequency (%)
2,01713E+131
 
3.1%
2,27395E+131
 
3.1%
8,75681E+131
 
3.1%
8,62743E+131
 
3.1%
8,12246E+131
 
3.1%
7,63132E+131
 
3.1%
7,51171E+131
 
3.1%
7,95755E+131
 
3.1%
7,74432E+131
 
3.1%
7,53116E+131
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.756428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,01713e+131
 
3.1%
2,27395e+131
 
3.1%
2,3707e+131
 
3.1%
2,53937e+131
 
3.1%
2,58225e+131
 
3.1%
2,78724e+131
 
3.1%
3,10438e+131
 
3.1%
3,17363e+131
 
3.1%
3,16198e+131
 
3.1%
3,15397e+131
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
363
18.1%
157
16.4%
734
9.8%
,32
9.2%
E32
9.2%
+32
9.2%
518
 
5.2%
217
 
4.9%
416
 
4.6%
815
 
4.3%
Other values (3)32
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number252
72.4%
Other Punctuation32
 
9.2%
Uppercase Letter32
 
9.2%
Math Symbol32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
363
25.0%
157
22.6%
734
13.5%
518
 
7.1%
217
 
6.7%
416
 
6.3%
815
 
6.0%
615
 
6.0%
910
 
4.0%
07
 
2.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common316
90.8%
Latin32
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
363
19.9%
157
18.0%
734
10.8%
,32
10.1%
+32
10.1%
518
 
5.7%
217
 
5.4%
416
 
5.1%
815
 
4.7%
615
 
4.7%
Other values (2)17
 
5.4%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
363
18.1%
157
16.4%
734
9.8%
,32
9.2%
E32
9.2%
+32
9.2%
518
 
5.2%
217
 
4.9%
416
 
4.6%
815
 
4.3%
Other values (3)32
9.2%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,532430534
 
1
2,518432707
 
1
1,269146867
 
1
1,171816792
 
1
1,154049407
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,532430534
2nd row2,518432707
3rd row1,997834988
4th row1,860479941
5th row1,693538323

Common Values

ValueCountFrequency (%)
2,5324305341
 
3.1%
2,5184327071
 
3.1%
1,2691468671
 
3.1%
1,1718167921
 
3.1%
1,1540494071
 
3.1%
1,1496544851
 
3.1%
1,1373384271
 
3.1%
1,1499419971
 
3.1%
1,1852579011
 
3.1%
1,2416774791
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.855450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,5324305341
 
3.1%
2,5184327071
 
3.1%
1,9978349881
 
3.1%
1,8604799411
 
3.1%
1,6935383231
 
3.1%
1,5502764331
 
3.1%
1,4950220141
 
3.1%
1,465858461
 
3.1%
1,4093017331
 
3.1%
1,3908947861
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
155
15.7%
341
11.7%
,32
9.1%
432
9.1%
932
9.1%
028
8.0%
628
8.0%
727
7.7%
226
7.4%
525
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number319
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
155
17.2%
341
12.9%
432
10.0%
932
10.0%
028
8.8%
628
8.8%
727
8.5%
226
8.2%
525
7.8%
825
7.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
155
15.7%
341
11.7%
,32
9.1%
432
9.1%
932
9.1%
028
8.0%
628
8.0%
727
7.7%
226
7.4%
525
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
155
15.7%
341
11.7%
,32
9.1%
432
9.1%
932
9.1%
028
8.0%
628
8.0%
727
7.7%
226
7.4%
525
7.1%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,879935312
 
1
2,805267627
 
1
1,845491196
 
1
1,845053399
 
1
1,908642012
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.75
Min length9

Characters and Unicode

Total characters344
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,879935312
2nd row2,805267627
3rd row2,812136438
4th row2,690660711
5th row2,689534806

Common Values

ValueCountFrequency (%)
2,8799353121
 
3.1%
2,8052676271
 
3.1%
1,8454911961
 
3.1%
1,8450533991
 
3.1%
1,9086420121
 
3.1%
1,9172823681
 
3.1%
1,8722582811
 
3.1%
1,8629614391
 
3.1%
1,8498759181
 
3.1%
1,87108061
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:07.957473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,8799353121
 
3.1%
2,8052676271
 
3.1%
2,8121364381
 
3.1%
2,6906607111
 
3.1%
2,6895348061
 
3.1%
2,660374961
 
3.1%
2,4924961891
 
3.1%
2,4144739511
 
3.1%
2,3754096531
 
3.1%
2,2262083071
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
244
12.8%
139
11.3%
938
11.0%
833
9.6%
,32
9.3%
030
8.7%
628
8.1%
327
7.8%
426
7.6%
724
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312
90.7%
Other Punctuation32
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
244
14.1%
139
12.5%
938
12.2%
833
10.6%
030
9.6%
628
9.0%
327
8.7%
426
8.3%
724
7.7%
523
7.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
244
12.8%
139
11.3%
938
11.0%
833
9.6%
,32
9.3%
030
8.7%
628
8.1%
327
7.8%
426
7.6%
724
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244
12.8%
139
11.3%
938
11.0%
833
9.6%
,32
9.3%
030
8.7%
628
8.1%
327
7.8%
426
7.6%
724
7.0%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,02642732
 
1
1,880798413
 
1
1,316566978
 
1
1,359542751
 
1
1,357370559
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.90625
Min length10

Characters and Unicode

Total characters349
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,02642732
2nd row1,880798413
3rd row1,858852684
4th row1,751976983
5th row1,786767973

Common Values

ValueCountFrequency (%)
2,026427321
 
3.1%
1,8807984131
 
3.1%
1,3165669781
 
3.1%
1,3595427511
 
3.1%
1,3573705591
 
3.1%
1,3348352521
 
3.1%
1,2082283261
 
3.1%
1,2829696541
 
3.1%
1,3990204191
 
3.1%
1,4269249771
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.057496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,026427321
 
3.1%
1,8807984131
 
3.1%
1,8588526841
 
3.1%
1,7519769831
 
3.1%
1,7867679731
 
3.1%
1,713742761
 
3.1%
1,4678431431
 
3.1%
1,5884922791
 
3.1%
1,6266927991
 
3.1%
1,6445088791
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
145
12.9%
739
11.2%
536
10.3%
934
9.7%
232
9.2%
,32
9.2%
631
8.9%
431
8.9%
828
8.0%
322
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number317
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
145
14.2%
739
12.3%
536
11.4%
934
10.7%
232
10.1%
631
9.8%
431
9.8%
828
8.8%
322
6.9%
019
6.0%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common349
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
145
12.9%
739
11.2%
536
10.3%
934
9.7%
232
9.2%
,32
9.2%
631
8.9%
431
8.9%
828
8.0%
322
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
145
12.9%
739
11.2%
536
10.3%
934
9.7%
232
9.2%
,32
9.2%
631
8.9%
431
8.9%
828
8.0%
322
6.3%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
0,940781036
 
1
0,941791766
 
1
0,937281087
 
1
0,940993692
 
1
0,931980259
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length9

Characters and Unicode

Total characters347
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row0,940781036
2nd row0,941791766
3rd row0,940821904
4th row0,94831244
5th row0,950663825

Common Values

ValueCountFrequency (%)
0,9407810361
 
3.1%
0,9417917661
 
3.1%
0,9372810871
 
3.1%
0,9409936921
 
3.1%
0,9319802591
 
3.1%
0,9448382861
 
3.1%
0,959251331
 
3.1%
0,9669993341
 
3.1%
0,950866051
 
3.1%
0,9674272781
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.290548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,9407810361
 
3.1%
0,9417917661
 
3.1%
0,9408219041
 
3.1%
0,948312441
 
3.1%
0,9506638251
 
3.1%
0,92287181
 
3.1%
0,9168766171
 
3.1%
0,9112470761
 
3.1%
0,9204378981
 
3.1%
0,9385947751
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
050
14.4%
950
14.4%
833
9.5%
,32
9.2%
631
8.9%
730
8.6%
330
8.6%
425
7.2%
123
6.6%
523
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number315
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050
15.9%
950
15.9%
833
10.5%
631
9.8%
730
9.5%
330
9.5%
425
7.9%
123
7.3%
523
7.3%
220
 
6.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050
14.4%
950
14.4%
833
9.5%
,32
9.2%
631
8.9%
730
8.6%
330
8.6%
425
7.2%
123
6.6%
523
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050
14.4%
950
14.4%
833
9.5%
,32
9.2%
631
8.9%
730
8.6%
330
8.6%
425
7.2%
123
6.6%
523
6.6%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
1,934614309
 
1
1,958793742
 
1
1,27894142
 
1
1,324681094
 
1
1,351602232
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.8125
Min length9

Characters and Unicode

Total characters346
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row1,934614309
2nd row1,958793742
3rd row1,895444339
4th row1,8616877
5th row1,821753504

Common Values

ValueCountFrequency (%)
1,9346143091
 
3.1%
1,9587937421
 
3.1%
1,278941421
 
3.1%
1,3246810941
 
3.1%
1,3516022321
 
3.1%
1,1641615671
 
3.1%
1,1527093741
 
3.1%
0,9899252991
 
3.1%
1,00236721
 
3.1%
1,1184045981
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.393571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,9346143091
 
3.1%
1,9587937421
 
3.1%
1,8954443391
 
3.1%
1,86168771
 
3.1%
1,8217535041
 
3.1%
1,6966802571
 
3.1%
1,5540900711
 
3.1%
1,4037525811
 
3.1%
1,2462432021
 
3.1%
1,2562939021
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
165
18.8%
234
9.8%
,32
9.2%
831
9.0%
930
8.7%
328
8.1%
027
7.8%
527
7.8%
426
 
7.5%
623
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number314
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
165
20.7%
234
10.8%
831
9.9%
930
9.6%
328
8.9%
027
8.6%
527
8.6%
426
 
8.3%
623
 
7.3%
723
 
7.3%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
165
18.8%
234
9.8%
,32
9.2%
831
9.0%
930
8.7%
328
8.1%
027
7.8%
527
7.8%
426
 
7.5%
623
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
18.8%
234
9.8%
,32
9.2%
831
9.0%
930
8.7%
328
8.1%
027
7.8%
527
7.8%
426
 
7.5%
623
 
6.6%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct29
Distinct (%)100.0%
Missing3
Missing (%)9.4%
Memory size384.0 B
4,427032321
 
1
3,832115681
 
1
3,692518759
 
1
4,248996052
 
1
5,425147705
 
1
Other values (24)
24 

Length

Max length11
Median length11
Mean length10.96551724
Min length10

Characters and Unicode

Total characters318
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row4,427032321
2nd row4,181324647
3rd row4,523700655
4th row3,784429967
5th row3,756558821

Common Values

ValueCountFrequency (%)
4,4270323211
 
3.1%
3,8321156811
 
3.1%
3,6925187591
 
3.1%
4,2489960521
 
3.1%
5,4251477051
 
3.1%
4,8715147471
 
3.1%
4,1129929781
 
3.1%
3,8540425831
 
3.1%
3,6892404351
 
3.1%
3,4330438381
 
3.1%
Other values (19)19
59.4%
(Missing)3
 
9.4%

Length

2022-06-24T16:30:08.493593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,4270323211
 
3.4%
3,3301031921
 
3.4%
4,1813246471
 
3.4%
4,5237006551
 
3.4%
3,7844299671
 
3.4%
3,7565588211
 
3.4%
4,0398092971
 
3.4%
2,7326490891
 
3.4%
3,0732879221
 
3.4%
3,3070298371
 
3.4%
Other values (19)19
65.5%

Most occurring characters

ValueCountFrequency (%)
349
15.4%
435
11.0%
233
10.4%
,29
9.1%
929
9.1%
728
8.8%
826
8.2%
526
8.2%
024
7.5%
122
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number289
90.9%
Other Punctuation29
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
349
17.0%
435
12.1%
233
11.4%
929
10.0%
728
9.7%
826
9.0%
526
9.0%
024
8.3%
122
7.6%
617
 
5.9%
Other Punctuation
ValueCountFrequency (%)
,29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common318
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
349
15.4%
435
11.0%
233
10.4%
,29
9.1%
929
9.1%
728
8.8%
826
8.2%
526
8.2%
024
7.5%
122
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
15.4%
435
11.0%
233
10.4%
,29
9.1%
929
9.1%
728
8.8%
826
8.2%
526
8.2%
024
7.5%
122
6.9%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
5,871206008
 
1
5,605175294
 
1
3,427080181
 
1
3,316248808
 
1
3,313381294
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters350
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row5,871206008
2nd row5,605175294
3rd row4,883429398
4th row4,970466808
5th row4,604350295

Common Values

ValueCountFrequency (%)
5,8712060081
 
3.1%
5,6051752941
 
3.1%
3,4270801811
 
3.1%
3,3162488081
 
3.1%
3,3133812941
 
3.1%
3,4189423371
 
3.1%
3,4778451661
 
3.1%
3,695894651
 
3.1%
4,0466788791
 
3.1%
4,4774012191
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.592615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,8712060081
 
3.1%
5,6051752941
 
3.1%
4,8834293981
 
3.1%
4,9704668081
 
3.1%
4,6043502951
 
3.1%
4,2152646751
 
3.1%
3,8602457921
 
3.1%
3,5549822061
 
3.1%
3,4055622441
 
3.1%
3,2015584991
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
447
13.4%
335
10.0%
834
9.7%
,32
9.1%
032
9.1%
632
9.1%
129
8.3%
229
8.3%
728
8.0%
526
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number318
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
447
14.8%
335
11.0%
834
10.7%
032
10.1%
632
10.1%
129
9.1%
229
9.1%
728
8.8%
526
8.2%
926
8.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
447
13.4%
335
10.0%
834
9.7%
,32
9.1%
032
9.1%
632
9.1%
129
8.3%
229
8.3%
728
8.0%
526
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
447
13.4%
335
10.0%
834
9.7%
,32
9.1%
032
9.1%
632
9.1%
129
8.3%
229
8.3%
728
8.0%
526
7.4%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
4,040954332
 
1
3,983217695
 
1
2,013004573
 
1
1,948755708
 
1
1,946324996
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters352
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row4,040954332
2nd row3,983217695
3rd row4,116802423
4th row3,864717766
5th row3,590649512

Common Values

ValueCountFrequency (%)
4,0409543321
 
3.1%
3,9832176951
 
3.1%
2,0130045731
 
3.1%
1,9487557081
 
3.1%
1,9463249961
 
3.1%
1,9814382551
 
3.1%
2,0455031921
 
3.1%
2,1839062481
 
3.1%
2,2936402411
 
3.1%
2,4205647311
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.691638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,0409543321
 
3.1%
3,9832176951
 
3.1%
4,1168024231
 
3.1%
3,8647177661
 
3.1%
3,5906495121
 
3.1%
3,3819337181
 
3.1%
2,8541994911
 
3.1%
2,7245923221
 
3.1%
2,5601487721
 
3.1%
2,4959622751
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
260
17.0%
437
10.5%
533
9.4%
,32
9.1%
932
9.1%
330
8.5%
129
8.2%
627
7.7%
026
7.4%
725
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number320
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
260
18.8%
437
11.6%
533
10.3%
932
10.0%
330
9.4%
129
9.1%
627
8.4%
026
8.1%
725
7.8%
821
 
6.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
260
17.0%
437
10.5%
533
9.4%
,32
9.1%
932
9.1%
330
8.5%
129
8.2%
627
7.7%
026
7.4%
725
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
17.0%
437
10.5%
533
9.4%
,32
9.1%
932
9.1%
330
8.5%
129
8.2%
627
7.7%
026
7.4%
725
7.1%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,68625
 
1
2,363841025
 
1
1,407988516
 
1
1,494203353
 
1
1,414485611
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.6875
Min length7

Characters and Unicode

Total characters342
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,68625
2nd row2,363841025
3rd row1,963333333
4th row1,521528861
5th row1,927922815

Common Values

ValueCountFrequency (%)
2,686251
 
3.1%
2,3638410251
 
3.1%
1,4079885161
 
3.1%
1,4942033531
 
3.1%
1,4144856111
 
3.1%
1,3479754131
 
3.1%
1,3655171541
 
3.1%
1,3302444231
 
3.1%
1,3294460841
 
3.1%
1,3786564651
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.794661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,686251
 
3.1%
2,3638410251
 
3.1%
1,9633333331
 
3.1%
1,5215288611
 
3.1%
1,9279228151
 
3.1%
2,0160077891
 
3.1%
1,862136981
 
3.1%
1,65484351
 
3.1%
1,5776883161
 
3.1%
1,662291921
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
159
17.3%
438
11.1%
635
10.2%
333
9.6%
,32
9.4%
529
8.5%
228
8.2%
827
7.9%
926
7.6%
720
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number310
90.6%
Other Punctuation32
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
159
19.0%
438
12.3%
635
11.3%
333
10.6%
529
9.4%
228
9.0%
827
8.7%
926
8.4%
720
 
6.5%
015
 
4.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
159
17.3%
438
11.1%
635
10.2%
333
9.6%
,32
9.4%
529
8.5%
228
8.2%
827
7.9%
926
7.6%
720
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
17.3%
438
11.1%
635
10.2%
333
9.6%
,32
9.4%
529
8.5%
228
8.2%
827
7.9%
926
7.6%
720
 
5.8%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,534179728
 
1
3,14621461
 
1
2,519120764
 
1
2,433922067
 
1
2,531462588
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length9

Characters and Unicode

Total characters347
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,534179728
2nd row3,14621461
3rd row2,909974328
4th row2,704839706
5th row2,823542246

Common Values

ValueCountFrequency (%)
3,5341797281
 
3.1%
3,146214611
 
3.1%
2,5191207641
 
3.1%
2,4339220671
 
3.1%
2,5314625881
 
3.1%
2,5431511881
 
3.1%
2,4574506011
 
3.1%
2,5439825031
 
3.1%
2,5488256791
 
3.1%
2,6181676191
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:08.899684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,5341797281
 
3.1%
3,146214611
 
3.1%
2,9099743281
 
3.1%
2,7048397061
 
3.1%
2,8235422461
 
3.1%
2,6646737271
 
3.1%
2,5784835721
 
3.1%
2,4727682251
 
3.1%
2,6477324261
 
3.1%
2,7273114171
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
263
18.2%
436
10.4%
736
10.4%
635
10.1%
834
9.8%
,32
9.2%
325
 
7.2%
525
 
7.2%
124
 
6.9%
923
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number315
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
263
20.0%
436
11.4%
736
11.4%
635
11.1%
834
10.8%
325
 
7.9%
525
 
7.9%
124
 
7.6%
923
 
7.3%
014
 
4.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
263
18.2%
436
10.4%
736
10.4%
635
10.1%
834
9.8%
,32
9.2%
325
 
7.2%
525
 
7.2%
124
 
6.9%
923
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
263
18.2%
436
10.4%
736
10.4%
635
10.1%
834
9.8%
,32
9.2%
325
 
7.2%
525
 
7.2%
124
 
6.9%
923
 
6.6%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
0,517255829
 
1
0,433081035
 
1
0,52348249
 
1
0,477517407
 
1
0,436510296
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters350
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row0,517255829
2nd row0,433081035
3rd row0,435402301
4th row0,469454656
5th row0,442785494

Common Values

ValueCountFrequency (%)
0,5172558291
 
3.1%
0,4330810351
 
3.1%
0,523482491
 
3.1%
0,4775174071
 
3.1%
0,4365102961
 
3.1%
0,4950644141
 
3.1%
0,4666761221
 
3.1%
0,5138299571
 
3.1%
0,5079194551
 
3.1%
0,4759872811
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:09.002707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,5172558291
 
3.1%
0,4330810351
 
3.1%
0,4354023011
 
3.1%
0,4694546561
 
3.1%
0,4427854941
 
3.1%
0,5188303271
 
3.1%
0,4508915311
 
3.1%
0,4764847781
 
3.1%
0,4580958541
 
3.1%
0,4504504871
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
057
16.3%
447
13.4%
542
12.0%
,32
9.1%
730
8.6%
126
7.4%
625
7.1%
324
6.9%
823
6.6%
923
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number318
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
057
17.9%
447
14.8%
542
13.2%
730
9.4%
126
8.2%
625
7.9%
324
7.5%
823
7.2%
923
7.2%
221
 
6.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
057
16.3%
447
13.4%
542
12.0%
,32
9.1%
730
8.6%
126
7.4%
625
7.1%
324
6.9%
823
6.6%
923
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
057
16.3%
447
13.4%
542
12.0%
,32
9.1%
730
8.6%
126
7.4%
625
7.1%
324
6.9%
823
6.6%
923
6.6%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
4,357884741
 
1
3,896351478
 
1
0,978057397
 
1
0,984961398
 
1
1,030060552
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row4,357884741
2nd row3,896351478
3rd row3,222633894
4th row2,817650615
5th row2,495523698

Common Values

ValueCountFrequency (%)
4,3578847411
 
3.1%
3,8963514781
 
3.1%
0,9780573971
 
3.1%
0,9849613981
 
3.1%
1,0300605521
 
3.1%
1,0651996451
 
3.1%
1,0991820751
 
3.1%
1,1094378831
 
3.1%
1,1232742621
 
3.1%
1,1327931121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:09.102730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,3578847411
 
3.1%
3,8963514781
 
3.1%
3,2226338941
 
3.1%
2,8176506151
 
3.1%
2,4955236981
 
3.1%
2,5619659011
 
3.1%
2,1178640471
 
3.1%
1,7558787981
 
3.1%
1,5821335571
 
3.1%
1,3831667231
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
159
16.8%
337
10.5%
833
9.4%
,32
9.1%
532
9.1%
231
8.8%
730
8.5%
928
8.0%
024
6.8%
423
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number319
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
159
18.5%
337
11.6%
833
10.3%
532
10.0%
231
9.7%
730
9.4%
928
8.8%
024
7.5%
423
 
7.2%
622
 
6.9%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
159
16.8%
337
10.5%
833
9.4%
,32
9.1%
532
9.1%
231
8.8%
730
8.5%
928
8.0%
024
6.8%
423
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
16.8%
337
10.5%
833
9.4%
,32
9.1%
532
9.1%
231
8.8%
730
8.5%
928
8.0%
024
6.8%
423
 
6.6%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2,447101315
 
1
2,454016116
 
1
1,727827863
 
1
1,739533683
 
1
1,74645507
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length10

Characters and Unicode

Total characters347
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row2,447101315
2nd row2,454016116
3rd row2,311243412
4th row2,449542068
5th row1,928066874

Common Values

ValueCountFrequency (%)
2,4471013151
 
3.1%
2,4540161161
 
3.1%
1,7278278631
 
3.1%
1,7395336831
 
3.1%
1,746455071
 
3.1%
1,7706957191
 
3.1%
1,7507177671
 
3.1%
1,7286890681
 
3.1%
1,7028550961
 
3.1%
1,6933681631
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:09.201752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,4471013151
 
3.1%
2,4540161161
 
3.1%
2,3112434121
 
3.1%
2,4495420681
 
3.1%
1,9280668741
 
3.1%
1,6934795991
 
3.1%
1,6862338871
 
3.1%
1,652726671
 
3.1%
1,6326506731
 
3.1%
1,6550806911
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
151
14.7%
641
11.8%
739
11.2%
,32
9.2%
331
8.9%
429
8.4%
829
8.4%
527
7.8%
926
7.5%
224
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number315
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
151
16.2%
641
13.0%
739
12.4%
331
9.8%
429
9.2%
829
9.2%
527
8.6%
926
8.3%
224
7.6%
018
 
5.7%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
151
14.7%
641
11.8%
739
11.2%
,32
9.2%
331
8.9%
429
8.4%
829
8.4%
527
7.8%
926
7.5%
224
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
14.7%
641
11.8%
739
11.2%
,32
9.2%
331
8.9%
429
8.4%
829
8.4%
527
7.8%
926
7.5%
224
6.9%

Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,44479993
 
1
3,310428009
 
1
2,18686376
 
1
2,148120668
 
1
2,172198326
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.84375
Min length10

Characters and Unicode

Total characters347
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,44479993
2nd row3,310428009
3rd row3,010712767
4th row2,942947811
5th row2,766360016

Common Values

ValueCountFrequency (%)
3,444799931
 
3.1%
3,3104280091
 
3.1%
2,186863761
 
3.1%
2,1481206681
 
3.1%
2,1721983261
 
3.1%
2,2178533521
 
3.1%
2,2550980561
 
3.1%
2,2510887511
 
3.1%
2,304419431
 
3.1%
2,372881791
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:09.302774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,444799931
 
3.1%
3,3104280091
 
3.1%
3,0107127671
 
3.1%
2,9429478111
 
3.1%
2,7663600161
 
3.1%
2,5798263051
 
3.1%
2,3860503441
 
3.1%
2,3162145921
 
3.1%
2,3210668841
 
3.1%
2,2704647171
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
262
17.9%
436
10.4%
333
9.5%
,32
9.2%
131
8.9%
729
8.4%
629
8.4%
027
7.8%
825
7.2%
524
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number315
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
262
19.7%
436
11.4%
333
10.5%
131
9.8%
729
9.2%
629
9.2%
027
8.6%
825
7.9%
524
 
7.6%
919
 
6.0%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
262
17.9%
436
10.4%
333
9.5%
,32
9.2%
131
8.9%
729
8.4%
629
8.4%
027
7.8%
825
7.2%
524
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
262
17.9%
436
10.4%
333
9.5%
,32
9.2%
131
8.9%
729
8.4%
629
8.4%
027
7.8%
825
7.2%
524
 
6.9%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.812391656 × 1010
Minimum2.581540196 × 1010
Maximum5.27647612 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:09.402797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.581540196 × 1010
5-th percentile2.711016254 × 1010
Q13.265592355 × 1010
median3.845636009 × 1010
Q34.390933084 × 1010
95-th percentile4.758604204 × 1010
Maximum5.27647612 × 1010
Range2.694935924 × 1010
Interquartile range (IQR)1.125340729 × 1010

Descriptive statistics

Standard deviation6770960266
Coefficient of variation (CV)0.177604005
Kurtosis-0.632353619
Mean3.812391656 × 1010
Median Absolute Deviation (MAD)5622642143
Skewness0.02046413775
Sum1.21996533 × 1012
Variance4.584590293 × 1019
MonotonicityNot monotonic
2022-06-24T16:30:09.514822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.163160514 × 10101
 
3.1%
3.983466688 × 10101
 
3.1%
4.900751232 × 10101
 
3.1%
4.64230209 × 10101
 
3.1%
4.221025809 × 10101
 
3.1%
3.985504731 × 10101
 
3.1%
3.817002107 × 10101
 
3.1%
4.466283117 × 10101
 
3.1%
4.42426473 × 10101
 
3.1%
4.379822535 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.581540196 × 10101
3.1%
2.649759316 × 10101
3.1%
2.761135567 × 10101
3.1%
3.032496518 × 10101
3.1%
3.069015843 × 10101
3.1%
3.120076808 × 10101
3.1%
3.12677104 × 10101
3.1%
3.163160514 × 10101
3.1%
3.299736302 × 10101
3.1%
3.419770564 × 10101
3.1%
ValueCountFrequency (%)
5.27647612 × 10101
3.1%
4.900751232 × 10101
3.1%
4.64230209 × 10101
3.1%
4.516321288 × 10101
3.1%
4.509895631 × 10101
3.1%
4.466283117 × 10101
3.1%
4.452892784 × 10101
3.1%
4.42426473 × 10101
3.1%
4.379822535 × 10101
3.1%
4.302591501 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.291066133 × 1010
Minimum2.795156638 × 1010
Maximum5.64414554 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:09.619845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.795156638 × 1010
5-th percentile2.909741241 × 1010
Q13.577506255 × 1010
median4.448331507 × 1010
Q35.086580375 × 1010
95-th percentile5.468116369 × 1010
Maximum5.64414554 × 1010
Range2.848988901 × 1010
Interquartile range (IQR)1.50907412 × 1010

Descriptive statistics

Standard deviation8834793194
Coefficient of variation (CV)0.2058880688
Kurtosis-1.35285597
Mean4.291066133 × 1010
Median Absolute Deviation (MAD)7539446438
Skewness-0.1486786117
Sum1.373141163 × 1012
Variance7.805357078 × 1019
MonotonicityNot monotonic
2022-06-24T16:30:09.725869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.966545434 × 10101
 
3.1%
3.577443032 × 10101
 
3.1%
5.011892921 × 10101
 
3.1%
5.140981284 × 10101
 
3.1%
4.919566225 × 10101
 
3.1%
4.737058955 × 10101
 
3.1%
4.564747164 × 10101
 
3.1%
5.31347509 × 10101
 
3.1%
5.200146245 × 10101
 
3.1%
5.02165074 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.795156638 × 10101
3.1%
2.840313895 × 10101
3.1%
2.966545434 × 10101
3.1%
3.057839893 × 10101
3.1%
3.267271428 × 10101
3.1%
3.363356128 × 10101
3.1%
3.46979041 × 10101
3.1%
3.577443032 × 10101
3.1%
3.577527329 × 10101
3.1%
3.586912092 × 10101
3.1%
ValueCountFrequency (%)
5.64414554 × 10101
3.1%
5.536596584 × 10101
3.1%
5.412087101 × 10101
3.1%
5.31347509 × 10101
3.1%
5.274706486 × 10101
3.1%
5.204406056 × 10101
3.1%
5.200146245 × 10101
3.1%
5.140981284 × 10101
3.1%
5.068446738 × 10101
3.1%
5.02165074 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.529587696 × 1010
Minimum1.7185853 × 1010
Maximum3.683998975 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:09.832893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.7185853 × 1010
5-th percentile1.788832144 × 1010
Q12.077850302 × 1010
median2.570685094 × 1010
Q32.974848385 × 1010
95-th percentile3.393035933 × 1010
Maximum3.683998975 × 1010
Range1.965413674 × 1010
Interquartile range (IQR)8969980826

Descriptive statistics

Standard deviation5543314300
Coefficient of variation (CV)0.2191390442
Kurtosis-1.105495069
Mean2.529587696 × 1010
Median Absolute Deviation (MAD)4622309872
Skewness0.26417649
Sum8.094680627 × 1011
Variance3.072833343 × 1019
MonotonicityNot monotonic
2022-06-24T16:30:09.942917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.767548654 × 10101
 
3.1%
2.073462558 × 10101
 
3.1%
2.638067398 × 10101
 
3.1%
2.842009844 × 10101
 
3.1%
2.644789292 × 10101
 
3.1%
2.50330279 × 10101
 
3.1%
2.218084507 × 10101
 
3.1%
2.770103434 × 10101
 
3.1%
2.99574459 × 10101
 
3.1%
2.97810082 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.7185853 × 10101
3.1%
1.767548654 × 10101
3.1%
1.806245908 × 10101
3.1%
1.82420139 × 10101
3.1%
1.951938148 × 10101
3.1%
1.987872093 × 10101
3.1%
2.015610226 × 10101
3.1%
2.073462558 × 10101
3.1%
2.079312883 × 10101
3.1%
2.082512535 × 10101
3.1%
ValueCountFrequency (%)
3.683998975 × 10101
3.1%
3.405448132 × 10101
3.1%
3.382880497 × 10101
3.1%
3.202081995 × 10101
3.1%
3.198243179 × 10101
3.1%
3.026107666 × 10101
3.1%
2.99574459 × 10101
3.1%
2.97810082 × 10101
3.1%
2.973764239 × 10101
3.1%
2.963302226 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.419188023 × 1010
Minimum2.796635354 × 1010
Maximum6.076221384 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:10.052943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.796635354 × 1010
5-th percentile3.099218192 × 1010
Q14.072718558 × 1010
median4.479310371 × 1010
Q34.707985496 × 1010
95-th percentile5.706568649 × 1010
Maximum6.076221384 × 1010
Range3.27958603 × 1010
Interquartile range (IQR)6352669379

Descriptive statistics

Standard deviation7254506771
Coefficient of variation (CV)0.1641592694
Kurtosis0.9980385003
Mean4.419188023 × 1010
Median Absolute Deviation (MAD)3737151978
Skewness0.005061612337
Sum1.414140167 × 1012
Variance5.262786849 × 1019
MonotonicityNot monotonic
2022-06-24T16:30:10.159966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.796635354 × 10101
 
3.1%
2.880045168 × 10101
 
3.1%
4.760901999 × 10101
 
3.1%
4.661795486 × 10101
 
3.1%
4.53870318 × 10101
 
3.1%
4.647128771 × 10101
 
3.1%
4.210610331 × 10101
 
3.1%
4.690346661 × 10101
 
3.1%
4.902393241 × 10101
 
3.1%
6.00115302 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.796635354 × 10101
3.1%
2.880045168 × 10101
3.1%
3.278541575 × 10101
3.1%
3.599912358 × 10101
3.1%
3.784901264 × 10101
3.1%
3.933370817 × 10101
3.1%
4.053004569 × 10101
3.1%
4.063484061 × 10101
3.1%
4.075796723 × 10101
3.1%
4.135393622 × 10101
3.1%
ValueCountFrequency (%)
6.076221384 × 10101
3.1%
6.00115302 × 10101
3.1%
5.465545074 × 10101
3.1%
5.146515821 × 10101
3.1%
4.996167324 × 10101
3.1%
4.9148557 × 10101
3.1%
4.902393241 × 10101
3.1%
4.760901999 × 10101
3.1%
4.690346661 × 10101
3.1%
4.661795486 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.434377235 × 1010
Minimum7748607984
Maximum2.275484713 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:10.265990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7748607984
5-th percentile8091241279
Q19477259207
median1.220138241 × 1010
Q31.903109175 × 1010
95-th percentile2.247653039 × 1010
Maximum2.275484713 × 1010
Range1.500623914 × 1010
Interquartile range (IQR)9553832538

Descriptive statistics

Standard deviation5352565584
Coefficient of variation (CV)0.3731630323
Kurtosis-1.578318662
Mean1.434377235 × 1010
Median Absolute Deviation (MAD)4123423044
Skewness0.2582117747
Sum4.590007152 × 1011
Variance2.864995834 × 1019
MonotonicityNot monotonic
2022-06-24T16:30:10.376014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.074713469 × 10101
 
3.1%
1.141463185 × 10101
 
3.1%
2.220440844 × 10101
 
3.1%
2.272932758 × 10101
 
3.1%
2.226969632 × 10101
 
3.1%
1.778277554 × 10101
 
3.1%
1.79376419 × 10101
 
3.1%
1.785364048 × 10101
 
3.1%
1.851573121 × 10101
 
3.1%
2.045210711 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
77486079841
3.1%
79451401831
3.1%
82107785401
3.1%
82993852311
3.1%
83755714251
3.1%
84953992811
3.1%
86158844711
3.1%
91769039081
3.1%
95773776401
3.1%
99582456021
3.1%
ValueCountFrequency (%)
2.275484713 × 10101
3.1%
2.272932758 × 10101
3.1%
2.226969632 × 10101
3.1%
2.220440844 × 10101
3.1%
2.139372086 × 10101
3.1%
2.045210711 × 10101
3.1%
1.93420584 × 10101
3.1%
1.931568882 × 10101
3.1%
1.893622605 × 10101
3.1%
1.851573121 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)100.0%
Missing4
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean4.079837324 × 1010
Minimum6469035211
Maximum8.835289646 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:10.481053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6469035211
5-th percentile7832873691
Q11.384483673 × 1010
median3.902638831 × 1010
Q36.550645743 × 1010
95-th percentile8.3567018 × 1010
Maximum8.835289646 × 1010
Range8.188386125 × 1010
Interquartile range (IQR)5.16616207 × 1010

Descriptive statistics

Standard deviation2.775021763 × 1010
Coefficient of variation (CV)0.6801795126
Kurtosis-1.561614465
Mean4.079837324 × 1010
Median Absolute Deviation (MAD)2.582673206 × 1010
Skewness0.1942083782
Sum1.142354451 × 1012
Variance7.700745783 × 1020
MonotonicityNot monotonic
2022-06-24T16:30:10.717106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.3534995 × 10101
 
3.1%
6.520133585 × 10101
 
3.1%
6.160920476 × 10101
 
3.1%
6.691303354 × 10101
 
3.1%
6.924529455 × 10101
 
3.1%
6.642182218 × 10101
 
3.1%
8.469650465 × 10101
 
3.1%
8.835289646 × 10101
 
3.1%
8.146939993 × 10101
 
3.1%
7.023752395 × 10101
 
3.1%
Other values (18)18
56.2%
(Missing)4
 
12.5%
ValueCountFrequency (%)
64690352111
3.1%
77667200781
3.1%
79557304011
3.1%
92282041441
3.1%
1.168315134 × 10101
3.1%
1.274162947 × 10101
3.1%
1.354787173 × 10101
3.1%
1.394382506 × 10101
3.1%
1.582634065 × 10101
3.1%
1.697373908 × 10101
3.1%
ValueCountFrequency (%)
8.835289646 × 10101
3.1%
8.469650465 × 10101
3.1%
8.146939993 × 10101
3.1%
7.023752395 × 10101
3.1%
6.924529455 × 10101
3.1%
6.691303354 × 10101
3.1%
6.642182218 × 10101
3.1%
6.520133585 × 10101
3.1%
6.171253717 × 10101
3.1%
6.160920476 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
3,21867E+11
 
1
3,25129E+11
 
1
7,34344E+11
 
1
6,82491E+11
 
1
6,46753E+11
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row3,21867E+11
2nd row3,25129E+11
3rd row2,99373E+11
4th row3,25034E+11
5th row3,16719E+11

Common Values

ValueCountFrequency (%)
3,21867E+111
 
3.1%
3,25129E+111
 
3.1%
7,34344E+111
 
3.1%
6,82491E+111
 
3.1%
6,46753E+111
 
3.1%
6,39856E+111
 
3.1%
6,3383E+111
 
3.1%
6,47789E+111
 
3.1%
6,79229E+111
 
3.1%
7,25205E+111
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:10.819129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,21867e+111
 
3.1%
3,25129e+111
 
3.1%
2,99373e+111
 
3.1%
3,25034e+111
 
3.1%
3,16719e+111
 
3.1%
3,08084e+111
 
3.1%
2,95853e+111
 
3.1%
2,87961e+111
 
3.1%
2,93168e+111
 
3.1%
2,90996e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
173
20.8%
,32
9.1%
E32
9.1%
+32
9.1%
330
8.5%
223
 
6.6%
923
 
6.6%
821
 
6.0%
521
 
6.0%
620
 
5.7%
Other values (3)44
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number255
72.6%
Other Punctuation32
 
9.1%
Uppercase Letter32
 
9.1%
Math Symbol32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
173
28.6%
330
11.8%
223
 
9.0%
923
 
9.0%
821
 
8.2%
521
 
8.2%
620
 
7.8%
718
 
7.1%
014
 
5.5%
412
 
4.7%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common319
90.9%
Latin32
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
173
22.9%
,32
10.0%
+32
10.0%
330
9.4%
223
 
7.2%
923
 
7.2%
821
 
6.6%
521
 
6.6%
620
 
6.3%
718
 
5.6%
Other values (2)26
 
8.2%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
173
20.8%
,32
9.1%
E32
9.1%
+32
9.1%
330
8.5%
223
 
6.6%
923
 
6.6%
821
 
6.0%
521
 
6.0%
620
 
5.7%
Other values (3)44
12.5%

Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.264236898 × 1010
Minimum3.745664681 × 1010
Maximum7.344803201 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:10.922152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.745664681 × 1010
5-th percentile3.821279875 × 1010
Q14.054278736 × 1010
median5.283522496 × 1010
Q36.387307163 × 1010
95-th percentile6.965944121 × 1010
Maximum7.344803201 × 1010
Range3.59913852 × 1010
Interquartile range (IQR)2.333028427 × 1010

Descriptive statistics

Standard deviation1.171119934 × 1010
Coefficient of variation (CV)0.2224671793
Kurtosis-1.432958308
Mean5.264236898 × 1010
Median Absolute Deviation (MAD)1.127883814 × 1010
Skewness0.1309113663
Sum1.684555807 × 1012
Variance1.3715219 × 1020
MonotonicityNot monotonic
2022-06-24T16:30:11.028176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.745664681 × 10101
 
3.1%
4.354509484 × 10101
 
3.1%
5.685613307 × 10101
 
3.1%
5.568022822 × 10101
 
3.1%
5.163353922 × 10101
 
3.1%
5.332732763 × 10101
 
3.1%
5.999020572 × 10101
 
3.1%
6.699546865 × 10101
 
3.1%
6.383772486 × 10101
 
3.1%
6.545248755 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.745664681 × 10101
3.1%
3.811320679 × 10101
3.1%
3.829428308 × 10101
3.1%
3.856613464 × 10101
3.1%
3.85690135 × 10101
3.1%
3.934371127 × 10101
3.1%
3.951257994 × 10101
3.1%
3.988994745 × 10101
3.1%
4.076040067 × 10101
3.1%
4.122212583 × 10101
3.1%
ValueCountFrequency (%)
7.344803201 × 10101
3.1%
7.291540767 × 10101
3.1%
6.699546865 × 10101
3.1%
6.656955257 × 10101
3.1%
6.545248755 × 10101
3.1%
6.421761953 × 10101
3.1%
6.401050667 × 10101
3.1%
6.397911197 × 10101
3.1%
6.383772486 × 10101
3.1%
6.165363504 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.834397783 × 1010
Minimum4993804467
Maximum3.69362099 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:11.135200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4993804467
5-th percentile6917543918
Q19845258336
median1.433813833 × 1010
Q32.571332523 × 1010
95-th percentile3.39941778 × 1010
Maximum3.69362099 × 1010
Range3.194240543 × 1010
Interquartile range (IQR)1.58680669 × 1010

Descriptive statistics

Standard deviation9656735959
Coefficient of variation (CV)0.5264254049
Kurtosis-1.16061925
Mean1.834397783 × 1010
Median Absolute Deviation (MAD)6046426066
Skewness0.4716993159
Sum5.870072907 × 1011
Variance9.325254938 × 1019
MonotonicityNot monotonic
2022-06-24T16:30:11.241223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
87614869661
 
3.1%
92362969551
 
3.1%
2.59068712 × 10101
 
3.1%
2.817740687 × 10101
 
3.1%
2.92618331 × 10101
 
3.1%
2.42247469 × 10101
 
3.1%
2.461770168 × 10101
 
3.1%
3.266023937 × 10101
 
3.1%
3.287478723 × 10101
 
3.1%
3.398700507 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
49938044671
3.1%
66946652951
3.1%
70998991551
3.1%
83929058841
3.1%
87614869661
3.1%
92362969551
3.1%
96645619031
3.1%
97801115851
3.1%
98669739191
3.1%
1.059149912 × 10101
3.1%
ValueCountFrequency (%)
3.69362099 × 10101
3.1%
3.400294447 × 10101
3.1%
3.398700507 × 10101
3.1%
3.287478723 × 10101
3.1%
3.266023937 × 10101
3.1%
2.92618331 × 10101
3.1%
2.817740687 × 10101
3.1%
2.59068712 × 10101
3.1%
2.564880991 × 10101
3.1%
2.461770168 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.010894516 × 1010
Minimum8083231410
Maximum7.28874466 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:11.346247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8083231410
5-th percentile8456454796
Q11.124611177 × 1010
median2.165543973 × 1010
Q34.796110055 × 1010
95-th percentile6.860279618 × 1010
Maximum7.28874466 × 1010
Range6.480421519 × 1010
Interquartile range (IQR)3.671498878 × 1010

Descriptive statistics

Standard deviation2.158137355 × 1010
Coefficient of variation (CV)0.716776142
Kurtosis-1.021211832
Mean3.010894516 × 1010
Median Absolute Deviation (MAD)1.23379318 × 1010
Skewness0.6581160414
Sum9.634862451 × 1011
Variance4.657556843 × 1020
MonotonicityNot monotonic
2022-06-24T16:30:11.452271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.058979692 × 10101
 
3.1%
1.053703545 × 10101
 
3.1%
7.146890052 × 10101
 
3.1%
6.625780172 × 10101
 
3.1%
6.455943528 × 10101
 
3.1%
5.663762264 × 10101
 
3.1%
5.129548375 × 10101
 
3.1%
5.091409628 × 10101
 
3.1%
4.74035288 × 10101
 
3.1%
4.721692005 × 10101
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
80832314101
3.1%
82535425811
3.1%
86224738811
3.1%
88805512261
3.1%
97544646301
3.1%
99046727361
3.1%
1.053703545 × 10101
3.1%
1.058979692 × 10101
3.1%
1.146488339 × 10101
3.1%
1.192061082 × 10101
3.1%
ValueCountFrequency (%)
7.28874466 × 10101
3.1%
7.146890052 × 10101
3.1%
6.625780172 × 10101
3.1%
6.455943528 × 10101
3.1%
5.663762264 × 10101
3.1%
5.129548375 × 10101
3.1%
5.091409628 × 10101
3.1%
4.963381579 × 10101
3.1%
4.74035288 × 10101
3.1%
4.721692005 × 10101
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3723166587
Minimum1153375828
Maximum6758693845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:11.556294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1153375828
5-th percentile1347417448
Q12243432850
median3147861856
Q35369866258
95-th percentile6553093122
Maximum6758693845
Range5605318017
Interquartile range (IQR)3126433408

Descriptive statistics

Standard deviation1748703696
Coefficient of variation (CV)0.4696818301
Kurtosis-1.25217771
Mean3723166587
Median Absolute Deviation (MAD)1344841924
Skewness0.2659252481
Sum1.191413308 × 1011
Variance3.057964617 × 1018
MonotonicityNot monotonic
2022-06-24T16:30:11.664318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
11533758281
 
3.1%
12108725021
 
3.1%
66508082541
 
3.1%
58395212711
 
3.1%
50620766461
 
3.1%
53368757401
 
3.1%
54688378121
 
3.1%
67586938451
 
3.1%
64731443781
 
3.1%
57170355751
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
11533758281
3.1%
12108725021
3.1%
14591360411
3.1%
15626153721
3.1%
18245500661
3.1%
18828731031
3.1%
21229803381
3.1%
21840610421
3.1%
22632234531
3.1%
26352840791
3.1%
ValueCountFrequency (%)
67586938451
3.1%
66508082541
3.1%
64731443781
3.1%
61163765821
3.1%
58395212711
3.1%
57170355751
3.1%
54984585421
3.1%
54688378121
3.1%
53368757401
3.1%
50620766461
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3284237975
Minimum1738036625
Maximum4594154078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-06-24T16:30:11.770342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1738036625
5-th percentile1785981534
Q12972245626
median3483725120
Q33726658630
95-th percentile4420767555
Maximum4594154078
Range2856117453
Interquartile range (IQR)754413003

Descriptive statistics

Standard deviation816510128.8
Coefficient of variation (CV)0.2486147882
Kurtosis-0.4218344789
Mean3284237975
Median Absolute Deviation (MAD)387675005
Skewness-0.5799014714
Sum1.050956152 × 1011
Variance6.666887904 × 1017
MonotonicityNot monotonic
2022-06-24T16:30:11.876366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
41818864671
 
3.1%
43644582041
 
3.1%
34357360371
 
3.1%
36229187431
 
3.1%
35915076131
 
3.1%
31393121281
 
3.1%
34888679481
 
3.1%
38924850911
 
3.1%
41182084831
 
3.1%
44895900961
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
17380366251
3.1%
17660828981
3.1%
18022622361
3.1%
18917250131
3.1%
19056560091
3.1%
24141377101
3.1%
25741762781
3.1%
25917871311
3.1%
30990651251
3.1%
31393121281
3.1%
ValueCountFrequency (%)
45941540781
3.1%
44895900961
3.1%
43644582041
3.1%
41881680921
3.1%
41818864671
3.1%
41182084831
3.1%
38924850911
3.1%
38744151351
3.1%
36774064611
3.1%
36229187431
3.1%

Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
11251332630
 
1
9926349251
 
1
2,40333E+11
 
1
2,32531E+11
 
1
2,10443E+11
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters348
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row11251332630
2nd row9926349251
3rd row9802375366
4th row12244267842
5th row12360225861

Common Values

ValueCountFrequency (%)
112513326301
 
3.1%
99263492511
 
3.1%
2,40333E+111
 
3.1%
2,32531E+111
 
3.1%
2,10443E+111
 
3.1%
1,98538E+111
 
3.1%
1,96539E+111
 
3.1%
1,82109E+111
 
3.1%
1,6407E+111
 
3.1%
1,45128E+111
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:11.980389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
112513326301
 
3.1%
99263492511
 
3.1%
98023753661
 
3.1%
122442678421
 
3.1%
123602258611
 
3.1%
98671200661
 
3.1%
123851294751
 
3.1%
142754008201
 
3.1%
156995867691
 
3.1%
170317802001
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
161
17.5%
243
12.4%
332
9.2%
032
9.2%
530
8.6%
627
7.8%
426
7.5%
922
 
6.3%
822
 
6.3%
720
 
5.7%
Other values (3)33
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number315
90.5%
Other Punctuation11
 
3.2%
Uppercase Letter11
 
3.2%
Math Symbol11
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
161
19.4%
243
13.7%
332
10.2%
032
10.2%
530
9.5%
627
8.6%
426
8.3%
922
 
7.0%
822
 
7.0%
720
 
6.3%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%
Uppercase Letter
ValueCountFrequency (%)
E11
100.0%
Math Symbol
ValueCountFrequency (%)
+11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common337
96.8%
Latin11
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
161
18.1%
243
12.8%
332
9.5%
032
9.5%
530
8.9%
627
8.0%
426
7.7%
922
 
6.5%
822
 
6.5%
720
 
5.9%
Other values (2)22
 
6.5%
Latin
ValueCountFrequency (%)
E11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
161
17.5%
243
12.4%
332
9.2%
032
9.2%
530
8.6%
627
7.8%
426
7.5%
922
 
6.3%
822
 
6.3%
720
 
5.7%
Other values (3)33
9.5%

Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
6,58524E+11
 
1
7,13011E+11
 
1
1,85993E+12
 
1
1,80195E+12
 
1
1,71798E+12
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row6,58524E+11
2nd row7,13011E+11
3rd row6,98285E+11
4th row7,30299E+11
5th row6,98673E+11

Common Values

ValueCountFrequency (%)
6,58524E+111
 
3.1%
7,13011E+111
 
3.1%
1,85993E+121
 
3.1%
1,80195E+121
 
3.1%
1,71798E+121
 
3.1%
1,64914E+121
 
3.1%
1,65293E+121
 
3.1%
1,75419E+121
 
3.1%
1,75559E+121
 
3.1%
1,75775E+121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:12.079411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6,58524e+111
 
3.1%
7,13011e+111
 
3.1%
6,98285e+111
 
3.1%
7,30299e+111
 
3.1%
6,98673e+111
 
3.1%
7,07808e+111
 
3.1%
7,26591e+111
 
3.1%
7,24515e+111
 
3.1%
7,24579e+111
 
3.1%
7,06829e+111
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
175
21.4%
234
9.7%
,32
9.1%
E32
9.1%
+32
9.1%
528
 
8.0%
728
 
8.0%
921
 
6.0%
819
 
5.4%
414
 
4.0%
Other values (3)36
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number255
72.6%
Other Punctuation32
 
9.1%
Uppercase Letter32
 
9.1%
Math Symbol32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
175
29.4%
234
13.3%
528
 
11.0%
728
 
11.0%
921
 
8.2%
819
 
7.5%
414
 
5.5%
014
 
5.5%
613
 
5.1%
39
 
3.5%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%
Uppercase Letter
ValueCountFrequency (%)
E32
100.0%
Math Symbol
ValueCountFrequency (%)
+32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common319
90.9%
Latin32
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
175
23.5%
234
10.7%
,32
10.0%
+32
10.0%
528
 
8.8%
728
 
8.8%
921
 
6.6%
819
 
6.0%
414
 
4.4%
014
 
4.4%
Other values (2)22
 
6.9%
Latin
ValueCountFrequency (%)
E32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
21.4%
234
9.7%
,32
9.1%
E32
9.1%
+32
9.1%
528
 
8.0%
728
 
8.0%
921
 
6.0%
819
 
5.4%
414
 
4.0%
Other values (3)36
10.3%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Memory size384.0 B
8,3
5 
9,5
3 
9,4
3 
9,3
2 
9,6
2 
Other values (16)
17 

Length

Max length4
Median length3
Mean length3
Min length1

Characters and Unicode

Total characters96
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st row11,2
2nd row11,4
3rd row10,4
4th row10
5th row9,8

Common Values

ValueCountFrequency (%)
8,35
15.6%
9,53
 
9.4%
9,43
 
9.4%
9,32
 
6.2%
9,62
 
6.2%
8,62
 
6.2%
8,71
 
3.1%
8,81
 
3.1%
8,51
 
3.1%
8,41
 
3.1%
Other values (11)11
34.4%

Length

2022-06-24T16:30:12.180434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,35
15.6%
9,43
 
9.4%
9,53
 
9.4%
9,32
 
6.2%
9,62
 
6.2%
8,62
 
6.2%
8,91
 
3.1%
10,41
 
3.1%
101
 
3.1%
9,81
 
3.1%
Other values (11)11
34.4%

Most occurring characters

ValueCountFrequency (%)
,30
31.2%
816
16.7%
916
16.7%
37
 
7.3%
17
 
7.3%
46
 
6.2%
54
 
4.2%
64
 
4.2%
72
 
2.1%
22
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number66
68.8%
Other Punctuation30
31.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
816
24.2%
916
24.2%
37
10.6%
17
10.6%
46
 
9.1%
54
 
6.1%
64
 
6.1%
72
 
3.0%
22
 
3.0%
02
 
3.0%
Other Punctuation
ValueCountFrequency (%)
,30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,30
31.2%
816
16.7%
916
16.7%
37
 
7.3%
17
 
7.3%
46
 
6.2%
54
 
4.2%
64
 
4.2%
72
 
2.1%
22
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,30
31.2%
816
16.7%
916
16.7%
37
 
7.3%
17
 
7.3%
46
 
6.2%
54
 
4.2%
64
 
4.2%
72
 
2.1%
22
 
2.1%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size384.0 B
12,8
8 
12,9
4 
13,1
3 
12,4
2 
12,7
2 
Other values (13)
13 

Length

Max length4
Median length4
Mean length3.875
Min length2

Characters and Unicode

Total characters124
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)40.6%

Sample

1st row13,6
2nd row13,4
3rd row13,2
4th row13,1
5th row13

Common Values

ValueCountFrequency (%)
12,88
25.0%
12,94
12.5%
13,13
 
9.4%
12,42
 
6.2%
12,72
 
6.2%
13,61
 
3.1%
11,21
 
3.1%
11,31
 
3.1%
11,51
 
3.1%
11,81
 
3.1%
Other values (8)8
25.0%

Length

2022-06-24T16:30:12.284457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12,88
25.0%
12,94
12.5%
13,13
 
9.4%
12,42
 
6.2%
12,72
 
6.2%
13,31
 
3.1%
13,21
 
3.1%
131
 
3.1%
12,51
 
3.1%
13,41
 
3.1%
Other values (8)8
25.0%

Most occurring characters

ValueCountFrequency (%)
139
31.5%
,30
24.2%
221
16.9%
310
 
8.1%
89
 
7.3%
95
 
4.0%
43
 
2.4%
72
 
1.6%
62
 
1.6%
52
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number94
75.8%
Other Punctuation30
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
139
41.5%
221
22.3%
310
 
10.6%
89
 
9.6%
95
 
5.3%
43
 
3.2%
72
 
2.1%
62
 
2.1%
52
 
2.1%
01
 
1.1%
Other Punctuation
ValueCountFrequency (%)
,30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
139
31.5%
,30
24.2%
221
16.9%
310
 
8.1%
89
 
7.3%
95
 
4.0%
43
 
2.4%
72
 
1.6%
62
 
1.6%
52
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139
31.5%
,30
24.2%
221
16.9%
310
 
8.1%
89
 
7.3%
95
 
4.0%
43
 
2.4%
72
 
1.6%
62
 
1.6%
52
 
1.6%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size384.0 B
9,4
6 
9,5
3 
9,6
3 
9,9
2 
9,7
2 
Other values (13)
16 

Length

Max length3
Median length3
Mean length2.75
Min length1

Characters and Unicode

Total characters88
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)31.2%

Sample

1st row9,9
2nd row10
3rd row9,9
4th row10
5th row9,7

Common Values

ValueCountFrequency (%)
9,46
18.8%
9,53
 
9.4%
9,63
 
9.4%
9,92
 
6.2%
9,72
 
6.2%
9,22
 
6.2%
9,82
 
6.2%
102
 
6.2%
7,81
 
3.1%
71
 
3.1%
Other values (8)8
25.0%

Length

2022-06-24T16:30:12.389480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,46
18.8%
9,63
 
9.4%
9,53
 
9.4%
9,92
 
6.2%
9,72
 
6.2%
9,22
 
6.2%
9,82
 
6.2%
102
 
6.2%
81
 
3.1%
9,31
 
3.1%
Other values (8)8
25.0%

Most occurring characters

ValueCountFrequency (%)
,27
30.7%
924
27.3%
87
 
8.0%
46
 
6.8%
76
 
6.8%
65
 
5.7%
54
 
4.5%
33
 
3.4%
22
 
2.3%
12
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number61
69.3%
Other Punctuation27
30.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
924
39.3%
87
 
11.5%
46
 
9.8%
76
 
9.8%
65
 
8.2%
54
 
6.6%
33
 
4.9%
22
 
3.3%
12
 
3.3%
02
 
3.3%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,27
30.7%
924
27.3%
87
 
8.0%
46
 
6.8%
76
 
6.8%
65
 
5.7%
54
 
4.5%
33
 
3.4%
22
 
2.3%
12
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,27
30.7%
924
27.3%
87
 
8.0%
46
 
6.8%
76
 
6.8%
65
 
5.7%
54
 
4.5%
33
 
3.4%
22
 
2.3%
12
 
2.3%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct24
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
9,6
3 
9,3
3 
8
 
2
8,2
 
2
8,5
 
2
Other values (19)
20 

Length

Max length4
Median length3
Mean length2.9375
Min length1

Characters and Unicode

Total characters94
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)56.2%

Sample

1st row10,2
2nd row10
3rd row9,9
4th row9,8
5th row9,6

Common Values

ValueCountFrequency (%)
9,63
 
9.4%
9,33
 
9.4%
82
 
6.2%
8,22
 
6.2%
8,52
 
6.2%
102
 
6.2%
10,21
 
3.1%
8,71
 
3.1%
71
 
3.1%
7,41
 
3.1%
Other values (14)14
43.8%

Length

2022-06-24T16:30:12.492503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,63
 
9.4%
9,33
 
9.4%
82
 
6.2%
8,22
 
6.2%
8,52
 
6.2%
102
 
6.2%
8,411
 
3.1%
9,91
 
3.1%
9,81
 
3.1%
9,541
 
3.1%
Other values (14)14
43.8%

Most occurring characters

ValueCountFrequency (%)
,27
28.7%
815
16.0%
914
14.9%
68
 
8.5%
35
 
5.3%
55
 
5.3%
75
 
5.3%
24
 
4.3%
14
 
4.3%
44
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number67
71.3%
Other Punctuation27
28.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
815
22.4%
914
20.9%
68
11.9%
35
 
7.5%
55
 
7.5%
75
 
7.5%
24
 
6.0%
14
 
6.0%
44
 
6.0%
03
 
4.5%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common94
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,27
28.7%
815
16.0%
914
14.9%
68
 
8.5%
35
 
5.3%
55
 
5.3%
75
 
5.3%
24
 
4.3%
14
 
4.3%
44
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII94
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,27
28.7%
815
16.0%
914
14.9%
68
 
8.5%
35
 
5.3%
55
 
5.3%
75
 
5.3%
24
 
4.3%
14
 
4.3%
44
 
4.3%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Memory size384.0 B
10,6
4 
10,8
3 
10,7
2 
11
2 
11,4
2 
Other values (18)
19 

Length

Max length4
Median length4
Mean length3.75
Min length2

Characters and Unicode

Total characters120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)53.1%

Sample

1st row14,9
2nd row15
3rd row14,4
4th row14,1
5th row13,5

Common Values

ValueCountFrequency (%)
10,64
 
12.5%
10,83
 
9.4%
10,72
 
6.2%
112
 
6.2%
11,42
 
6.2%
11,12
 
6.2%
10,51
 
3.1%
9,91
 
3.1%
10,11
 
3.1%
10,31
 
3.1%
Other values (13)13
40.6%

Length

2022-06-24T16:30:12.595526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10,64
 
12.5%
10,83
 
9.4%
10,72
 
6.2%
112
 
6.2%
11,42
 
6.2%
11,12
 
6.2%
151
 
3.1%
14,41
 
3.1%
14,11
 
3.1%
13,51
 
3.1%
Other values (13)13
40.6%

Most occurring characters

ValueCountFrequency (%)
143
35.8%
,29
24.2%
013
 
10.8%
48
 
6.7%
96
 
5.0%
35
 
4.2%
64
 
3.3%
83
 
2.5%
73
 
2.5%
53
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number91
75.8%
Other Punctuation29
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
143
47.3%
013
 
14.3%
48
 
8.8%
96
 
6.6%
35
 
5.5%
64
 
4.4%
83
 
3.3%
73
 
3.3%
53
 
3.3%
23
 
3.3%
Other Punctuation
ValueCountFrequency (%)
,29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
143
35.8%
,29
24.2%
013
 
10.8%
48
 
6.7%
96
 
5.0%
35
 
4.2%
64
 
3.3%
83
 
2.5%
73
 
2.5%
53
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
143
35.8%
,29
24.2%
013
 
10.8%
48
 
6.7%
96
 
5.0%
35
 
4.2%
64
 
3.3%
83
 
2.5%
73
 
2.5%
53
 
2.5%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size384.0 B
13,3
3 
10,2
 
2
10,4
 
1
10,1
 
1
10,9
 
1
Other values (24)
24 

Length

Max length4
Median length4
Mean length3.53125
Min length1

Characters and Unicode

Total characters113
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)84.4%

Sample

1st row14,6
2nd row13,4
3rd row12,1
4th row10,7
5th row9,4

Common Values

ValueCountFrequency (%)
13,33
 
9.4%
10,22
 
6.2%
10,41
 
3.1%
10,11
 
3.1%
10,91
 
3.1%
11,51
 
3.1%
12,91
 
3.1%
13,21
 
3.1%
12,61
 
3.1%
12,51
 
3.1%
Other values (19)19
59.4%

Length

2022-06-24T16:30:12.700550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13,33
 
9.4%
10,22
 
6.2%
13,41
 
3.1%
12,11
 
3.1%
10,71
 
3.1%
9,41
 
3.1%
9,51
 
3.1%
9,31
 
3.1%
8,91
 
3.1%
8,61
 
3.1%
Other values (19)19
59.4%

Most occurring characters

ValueCountFrequency (%)
,30
26.5%
125
22.1%
313
11.5%
29
 
8.0%
99
 
8.0%
07
 
6.2%
87
 
6.2%
44
 
3.5%
53
 
2.7%
63
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83
73.5%
Other Punctuation30
 
26.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
125
30.1%
313
15.7%
29
 
10.8%
99
 
10.8%
07
 
8.4%
87
 
8.4%
44
 
4.8%
53
 
3.6%
63
 
3.6%
73
 
3.6%
Other Punctuation
ValueCountFrequency (%)
,30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common113
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,30
26.5%
125
22.1%
313
11.5%
29
 
8.0%
99
 
8.0%
07
 
6.2%
87
 
6.2%
44
 
3.5%
53
 
2.7%
63
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,30
26.5%
125
22.1%
313
11.5%
29
 
8.0%
99
 
8.0%
07
 
6.2%
87
 
6.2%
44
 
3.5%
53
 
2.7%
63
 
2.7%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Memory size384.0 B
14
4 
14,3
3 
14,4
2 
14,2
2 
14,1
2 
Other values (18)
19 

Length

Max length4
Median length4
Mean length3.625
Min length2

Characters and Unicode

Total characters116
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)53.1%

Sample

1st row16,4
2nd row16,7
3rd row16,2
4th row15,8
5th row15,4

Common Values

ValueCountFrequency (%)
144
 
12.5%
14,33
 
9.4%
14,42
 
6.2%
14,22
 
6.2%
14,12
 
6.2%
12,42
 
6.2%
12,71
 
3.1%
11,41
 
3.1%
11,61
 
3.1%
11,81
 
3.1%
Other values (13)13
40.6%

Length

2022-06-24T16:30:12.805573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
144
 
12.5%
14,33
 
9.4%
14,42
 
6.2%
14,22
 
6.2%
14,12
 
6.2%
12,42
 
6.2%
131
 
3.1%
16,21
 
3.1%
15,81
 
3.1%
15,41
 
3.1%
Other values (13)13
40.6%

Most occurring characters

ValueCountFrequency (%)
137
31.9%
,26
22.4%
421
18.1%
210
 
8.6%
66
 
5.2%
35
 
4.3%
55
 
4.3%
72
 
1.7%
82
 
1.7%
01
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number90
77.6%
Other Punctuation26
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
137
41.1%
421
23.3%
210
 
11.1%
66
 
6.7%
35
 
5.6%
55
 
5.6%
72
 
2.2%
82
 
2.2%
01
 
1.1%
91
 
1.1%
Other Punctuation
ValueCountFrequency (%)
,26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
137
31.9%
,26
22.4%
421
18.1%
210
 
8.6%
66
 
5.2%
35
 
4.3%
55
 
4.3%
72
 
1.7%
82
 
1.7%
01
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
137
31.9%
,26
22.4%
421
18.1%
210
 
8.6%
66
 
5.2%
35
 
4.3%
55
 
4.3%
72
 
1.7%
82
 
1.7%
01
 
0.9%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size384.0 B
12,6
3 
11,9
3 
13,6
 
2
12,3
 
2
11,3
 
2
Other values (17)
20 

Length

Max length4
Median length4
Mean length3.75
Min length2

Characters and Unicode

Total characters120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)43.8%

Sample

1st row13,6
2nd row13,9
3rd row13,8
4th row13,6
5th row13,2

Common Values

ValueCountFrequency (%)
12,63
 
9.4%
11,93
 
9.4%
13,62
 
6.2%
12,32
 
6.2%
11,32
 
6.2%
12,82
 
6.2%
122
 
6.2%
12,92
 
6.2%
12,71
 
3.1%
10,71
 
3.1%
Other values (12)12
37.5%

Length

2022-06-24T16:30:12.907596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12,63
 
9.4%
11,93
 
9.4%
13,62
 
6.2%
12,32
 
6.2%
11,32
 
6.2%
12,82
 
6.2%
122
 
6.2%
12,92
 
6.2%
13,91
 
3.1%
13,81
 
3.1%
Other values (12)12
37.5%

Most occurring characters

ValueCountFrequency (%)
143
35.8%
,28
23.3%
216
 
13.3%
310
 
8.3%
96
 
5.0%
65
 
4.2%
84
 
3.3%
73
 
2.5%
02
 
1.7%
52
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number92
76.7%
Other Punctuation28
 
23.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
143
46.7%
216
 
17.4%
310
 
10.9%
96
 
6.5%
65
 
5.4%
84
 
4.3%
73
 
3.3%
02
 
2.2%
52
 
2.2%
41
 
1.1%
Other Punctuation
ValueCountFrequency (%)
,28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
143
35.8%
,28
23.3%
216
 
13.3%
310
 
8.3%
96
 
5.0%
65
 
4.2%
84
 
3.3%
73
 
2.5%
02
 
1.7%
52
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
143
35.8%
,28
23.3%
216
 
13.3%
310
 
8.3%
96
 
5.0%
65
 
4.2%
84
 
3.3%
73
 
2.5%
02
 
1.7%
52
 
1.7%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
25,534
 
1
24,844
 
1
13,703
 
1
13,924
 
1
14,125
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.9375
Min length5

Characters and Unicode

Total characters190
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row25,534
2nd row24,844
3rd row24,245
4th row23,721
5th row23,252

Common Values

ValueCountFrequency (%)
25,5341
 
3.1%
24,8441
 
3.1%
13,7031
 
3.1%
13,9241
 
3.1%
14,1251
 
3.1%
14,3071
 
3.1%
14,4721
 
3.1%
14,6241
 
3.1%
14,7721
 
3.1%
14,931
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.140648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25,5341
 
3.1%
24,8441
 
3.1%
24,2451
 
3.1%
23,7211
 
3.1%
23,2521
 
3.1%
22,8261
 
3.1%
22,4241
 
3.1%
22,0221
 
3.1%
21,6011
 
3.1%
21,1471
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
,32
16.8%
231
16.3%
131
16.3%
423
12.1%
515
7.9%
315
7.9%
715
7.9%
69
 
4.7%
07
 
3.7%
97
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number158
83.2%
Other Punctuation32
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
231
19.6%
131
19.6%
423
14.6%
515
9.5%
315
9.5%
715
9.5%
69
 
5.7%
07
 
4.4%
97
 
4.4%
85
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,32
16.8%
231
16.3%
131
16.3%
423
12.1%
515
7.9%
315
7.9%
715
7.9%
69
 
4.7%
07
 
3.7%
97
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,32
16.8%
231
16.3%
131
16.3%
423
12.1%
515
7.9%
315
7.9%
715
7.9%
69
 
4.7%
07
 
3.7%
97
 
3.7%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
32,121
 
1
31,517
 
1
17,644
 
1
17,857
 
1
18,083
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.96875
Min length5

Characters and Unicode

Total characters191
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row32,121
2nd row31,517
3rd row30,924
4th row30,348
5th row29,792

Common Values

ValueCountFrequency (%)
32,1211
 
3.1%
31,5171
 
3.1%
17,6441
 
3.1%
17,8571
 
3.1%
18,0831
 
3.1%
18,3321
 
3.1%
18,6251
 
3.1%
18,9841
 
3.1%
19,4161
 
3.1%
19,9231
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.239671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
32,1211
 
3.1%
31,5171
 
3.1%
30,9241
 
3.1%
30,3481
 
3.1%
29,7921
 
3.1%
29,2581
 
3.1%
28,7491
 
3.1%
28,2621
 
3.1%
27,7891
 
3.1%
27,3241
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
232
16.8%
,32
16.8%
121
11.0%
419
9.9%
916
8.4%
815
7.9%
314
7.3%
714
7.3%
512
 
6.3%
69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
83.2%
Other Punctuation32
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
232
20.1%
121
13.2%
419
11.9%
916
10.1%
815
9.4%
314
8.8%
714
8.8%
512
 
7.5%
69
 
5.7%
07
 
4.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
232
16.8%
,32
16.8%
121
11.0%
419
9.9%
916
8.4%
815
7.9%
314
7.3%
714
7.3%
512
 
6.3%
69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
232
16.8%
,32
16.8%
121
11.0%
419
9.9%
916
8.4%
815
7.9%
314
7.3%
714
7.3%
512
 
6.3%
69
 
4.7%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
29,352
 
1
28,802
 
1
17,297
 
1
17,602
 
1
17,918
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.90625
Min length5

Characters and Unicode

Total characters189
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row29,352
2nd row28,802
3rd row28,24
4th row27,681
5th row27,134

Common Values

ValueCountFrequency (%)
29,3521
 
3.1%
28,8021
 
3.1%
17,2971
 
3.1%
17,6021
 
3.1%
17,9181
 
3.1%
18,2451
 
3.1%
18,5731
 
3.1%
18,8921
 
3.1%
19,1981
 
3.1%
19,4881
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.336692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29,3521
 
3.1%
28,8021
 
3.1%
28,241
 
3.1%
27,6811
 
3.1%
27,1341
 
3.1%
26,6041
 
3.1%
26,0961
 
3.1%
25,6111
 
3.1%
25,1481
 
3.1%
24,7021
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
236
19.0%
,32
16.9%
121
11.1%
816
8.5%
014
 
7.4%
714
 
7.4%
413
 
6.9%
312
 
6.3%
911
 
5.8%
510
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157
83.1%
Other Punctuation32
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
236
22.9%
121
13.4%
816
10.2%
014
 
8.9%
714
 
8.9%
413
 
8.3%
312
 
7.6%
911
 
7.0%
510
 
6.4%
610
 
6.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
236
19.0%
,32
16.9%
121
11.1%
816
8.5%
014
 
7.4%
714
 
7.4%
413
 
6.9%
312
 
6.3%
911
 
5.8%
510
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
236
19.0%
,32
16.9%
121
11.1%
816
8.5%
014
 
7.4%
714
 
7.4%
413
 
6.9%
312
 
6.3%
911
 
5.8%
510
 
5.3%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
31,408
 
1
30,312
 
1
20,129
 
1
20,51
 
1
20,908
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.96875
Min length5

Characters and Unicode

Total characters191
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row31,408
2nd row30,312
3rd row29,194
4th row28,095
5th row27,055

Common Values

ValueCountFrequency (%)
31,4081
 
3.1%
30,3121
 
3.1%
20,1291
 
3.1%
20,511
 
3.1%
20,9081
 
3.1%
21,3141
 
3.1%
21,7191
 
3.1%
22,1131
 
3.1%
22,4831
 
3.1%
22,8151
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.435714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31,4081
 
3.1%
30,3121
 
3.1%
29,1941
 
3.1%
28,0951
 
3.1%
27,0551
 
3.1%
26,1071
 
3.1%
25,2761
 
3.1%
24,5721
 
3.1%
23,9831
 
3.1%
23,4991
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
244
23.0%
,32
16.8%
325
13.1%
118
9.4%
914
 
7.3%
013
 
6.8%
713
 
6.8%
510
 
5.2%
49
 
4.7%
88
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number159
83.2%
Other Punctuation32
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
244
27.7%
325
15.7%
118
11.3%
914
 
8.8%
013
 
8.2%
713
 
8.2%
510
 
6.3%
49
 
5.7%
88
 
5.0%
65
 
3.1%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
244
23.0%
,32
16.8%
325
13.1%
118
9.4%
914
 
7.3%
013
 
6.8%
713
 
6.8%
510
 
5.2%
49
 
4.7%
88
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244
23.0%
,32
16.8%
325
13.1%
118
9.4%
914
 
7.3%
013
 
6.8%
713
 
6.8%
510
 
5.2%
49
 
4.7%
88
 
4.2%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
21,58
 
1
21,06
 
1
10,41
 
1
10,86
 
1
12,64
 
1
Other values (27)
27 

Length

Max length5
Median length5
Mean length4.84375
Min length4

Characters and Unicode

Total characters155
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row21,58
2nd row21,06
3rd row19,68
4th row18,27
5th row18,09

Common Values

ValueCountFrequency (%)
21,581
 
3.1%
21,061
 
3.1%
10,411
 
3.1%
10,861
 
3.1%
12,641
 
3.1%
13,571
 
3.1%
11,991
 
3.1%
13,831
 
3.1%
13,031
 
3.1%
14,571
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.533736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,581
 
3.1%
21,061
 
3.1%
19,681
 
3.1%
18,271
 
3.1%
18,091
 
3.1%
17,71
 
3.1%
17,121
 
3.1%
16,981
 
3.1%
16,571
 
3.1%
15,641
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
139
25.2%
,32
20.6%
215
 
9.7%
810
 
6.5%
410
 
6.5%
69
 
5.8%
39
 
5.8%
99
 
5.8%
78
 
5.2%
57
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number123
79.4%
Other Punctuation32
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
139
31.7%
215
 
12.2%
810
 
8.1%
410
 
8.1%
69
 
7.3%
39
 
7.3%
99
 
7.3%
78
 
6.5%
57
 
5.7%
07
 
5.7%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
139
25.2%
,32
20.6%
215
 
9.7%
810
 
6.5%
410
 
6.5%
69
 
5.8%
39
 
5.8%
99
 
5.8%
78
 
5.2%
57
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139
25.2%
,32
20.6%
215
 
9.7%
810
 
6.5%
410
 
6.5%
69
 
5.8%
39
 
5.8%
99
 
5.8%
78
 
5.2%
57
 
4.5%

Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
26,27383124
 
1
25,87870079
 
1
17,87829381
 
1
18,14881146
 
1
18,66534705
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.96875
Min length10

Characters and Unicode

Total characters351
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row26,27383124
2nd row25,87870079
3rd row25,21133245
4th row24,5785773
5th row24,18120437

Common Values

ValueCountFrequency (%)
26,273831241
 
3.1%
25,878700791
 
3.1%
17,878293811
 
3.1%
18,148811461
 
3.1%
18,665347051
 
3.1%
19,071017241
 
3.1%
18,952225271
 
3.1%
19,464191861
 
3.1%
19,452447841
 
3.1%
19,954058181
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.632759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26,273831241
 
3.1%
25,878700791
 
3.1%
25,211332451
 
3.1%
24,57857731
 
3.1%
24,181204371
 
3.1%
23,804875941
 
3.1%
23,369458471
 
3.1%
23,081239561
 
3.1%
22,750777521
 
3.1%
22,337677081
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
146
13.1%
241
11.7%
735
10.0%
535
10.0%
,32
9.1%
832
9.1%
330
8.5%
028
8.0%
426
7.4%
623
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number319
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
146
14.4%
241
12.9%
735
11.0%
535
11.0%
832
10.0%
330
9.4%
028
8.8%
426
8.2%
623
7.2%
923
7.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
146
13.1%
241
11.7%
735
10.0%
535
10.0%
,32
9.1%
832
9.1%
330
8.5%
028
8.0%
426
7.4%
623
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
146
13.1%
241
11.7%
735
10.0%
535
10.0%
,32
9.1%
832
9.1%
330
8.5%
028
8.0%
426
7.4%
623
6.6%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
75,01314634
 
1
75,2277561
 
1
81,29268293
 
1
80,89268293
 
1
80,99268293
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.75
Min length10

Characters and Unicode

Total characters344
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row75,01314634
2nd row75,2277561
3rd row75,3195122
4th row75,8195122
5th row75,87073171

Common Values

ValueCountFrequency (%)
75,013146341
 
3.1%
75,22775611
 
3.1%
81,292682931
 
3.1%
80,892682931
 
3.1%
80,992682931
 
3.1%
80,99024391
 
3.1%
80,641463411
 
3.1%
81,09024391
 
3.1%
80,49024391
 
3.1%
80,539024391
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.731781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
75,013146341
 
3.1%
75,22775611
 
3.1%
75,31951221
 
3.1%
75,81951221
 
3.1%
75,870731711
 
3.1%
76,270731711
 
3.1%
76,421951221
 
3.1%
76,673170731
 
3.1%
77,073170731
 
3.1%
77,475609761
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
756
16.3%
839
11.3%
236
10.5%
935
10.2%
333
9.6%
,32
9.3%
025
7.3%
125
7.3%
623
6.7%
422
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312
90.7%
Other Punctuation32
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
756
17.9%
839
12.5%
236
11.5%
935
11.2%
333
10.6%
025
8.0%
125
8.0%
623
7.4%
422
 
7.1%
518
 
5.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
756
16.3%
839
11.3%
236
10.5%
935
10.2%
333
9.6%
,32
9.3%
025
7.3%
125
7.3%
623
6.7%
422
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
756
16.3%
839
11.3%
236
10.5%
935
10.2%
333
9.6%
,32
9.3%
025
7.3%
125
7.3%
623
6.7%
422
 
6.4%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size384.0 B
80,16341463
 
2
80,81219512
 
1
82,82682927
 
1
82,67560976
 
1
82,57560976
 
1
Other values (26)
26 

Length

Max length11
Median length11
Mean length10.28125
Min length4

Characters and Unicode

Total characters329
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)93.8%

Sample

1st row76,34878049
2nd row76,6
3rd row76,84878049
4th row77,1
5th row77,3

Common Values

ValueCountFrequency (%)
80,163414632
 
6.2%
80,812195121
 
3.1%
82,826829271
 
3.1%
82,675609761
 
3.1%
82,575609761
 
3.1%
82,573170731
 
3.1%
82,321951221
 
3.1%
82,71951221
 
3.1%
82,21951221
 
3.1%
81,968292681
 
3.1%
Other values (21)21
65.6%

Length

2022-06-24T16:30:13.833803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80,163414632
 
6.2%
76,61
 
3.1%
76,848780491
 
3.1%
77,11
 
3.1%
77,31
 
3.1%
77,648780491
 
3.1%
77,751219511
 
3.1%
77,953658541
 
3.1%
78,304878051
 
3.1%
78,604878051
 
3.1%
Other values (21)21
65.6%

Most occurring characters

ValueCountFrequency (%)
141
12.5%
741
12.5%
838
11.6%
635
10.6%
,32
9.7%
229
8.8%
529
8.8%
424
7.3%
924
7.3%
018
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number297
90.3%
Other Punctuation32
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
141
13.8%
741
13.8%
838
12.8%
635
11.8%
229
9.8%
529
9.8%
424
8.1%
924
8.1%
018
6.1%
318
6.1%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common329
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
141
12.5%
741
12.5%
838
11.6%
635
10.6%
,32
9.7%
229
8.8%
529
8.8%
424
7.3%
924
7.3%
018
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
12.5%
741
12.5%
838
11.6%
635
10.6%
,32
9.7%
229
8.8%
529
8.8%
424
7.3%
924
7.3%
018
5.5%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
76,8195122
 
1
76,97073171
 
1
83,49756098
 
1
83,34634146
 
1
82,94634146
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters346
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row76,8195122
2nd row76,97073171
3rd row77,0195122
4th row77,4195122
5th row77,72195122

Common Values

ValueCountFrequency (%)
76,81951221
 
3.1%
76,970731711
 
3.1%
83,497560981
 
3.1%
83,346341461
 
3.1%
82,946341461
 
3.1%
83,243902441
 
3.1%
82,543902441
 
3.1%
83,09024391
 
3.1%
82,69024391
 
3.1%
82,239024391
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:13.934826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
76,81951221
 
3.1%
76,970731711
 
3.1%
77,01951221
 
3.1%
77,41951221
 
3.1%
77,721951221
 
3.1%
77,921951221
 
3.1%
78,170731711
 
3.1%
78,521951221
 
3.1%
78,824390241
 
3.1%
78,975609761
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
848
13.9%
248
13.9%
737
10.7%
934
9.8%
433
9.5%
,32
9.2%
329
8.4%
126
7.5%
022
6.4%
620
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number314
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
848
15.3%
248
15.3%
737
11.8%
934
10.8%
433
10.5%
329
9.2%
126
8.3%
022
7.0%
620
6.4%
517
 
5.4%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
848
13.9%
248
13.9%
737
10.7%
934
9.8%
433
9.5%
,32
9.2%
329
8.4%
126
7.5%
022
6.4%
620
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
848
13.9%
248
13.9%
737
10.7%
934
9.8%
433
9.5%
,32
9.2%
329
8.4%
126
7.5%
022
6.4%
620
5.8%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
78,81804878
 
1
78,83682927
 
1
84,35634146
 
1
84,21097561
 
1
84,0997561
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.71875
Min length5

Characters and Unicode

Total characters343
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row78,81804878
2nd row78,83682927
3rd row79,10073171
4th row79,15390244
5th row79,29365854

Common Values

ValueCountFrequency (%)
78,818048781
 
3.1%
78,836829271
 
3.1%
84,356341461
 
3.1%
84,210975611
 
3.1%
84,09975611
 
3.1%
83,984878051
 
3.1%
83,793902441
 
3.1%
83,587804881
 
3.1%
83,331951221
 
3.1%
83,096097561
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.034848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78,818048781
 
3.1%
78,836829271
 
3.1%
79,100731711
 
3.1%
79,153902441
 
3.1%
79,293658541
 
3.1%
79,687073171
 
3.1%
79,536341461
 
3.1%
80,20024391
 
3.1%
80,424146341
 
3.1%
80,501463411
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
846
13.4%
137
10.8%
734
9.9%
,32
9.3%
331
9.0%
430
8.7%
029
8.5%
929
8.5%
227
7.9%
624
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number311
90.7%
Other Punctuation32
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
846
14.8%
137
11.9%
734
10.9%
331
10.0%
430
9.6%
029
9.3%
929
9.3%
227
8.7%
624
7.7%
524
7.7%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
846
13.4%
137
10.8%
734
9.9%
,32
9.3%
331
9.0%
430
8.7%
029
8.5%
929
8.5%
227
7.9%
624
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
846
13.4%
137
10.8%
734
9.9%
,32
9.3%
331
9.0%
430
8.7%
029
8.5%
929
8.5%
227
7.9%
624
7.0%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size384.0 B
81,9
3 
82,04878049
 
2
81,74878049
 
2
77,42195122
 
1
81,8
 
1
Other values (23)
23 

Length

Max length11
Median length11
Mean length10.03125
Min length4

Characters and Unicode

Total characters321
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)78.1%

Sample

1st row77,1195122
2nd row77,42195122
3rd row77,62195122
4th row77,72439024
5th row77,82439024

Common Values

ValueCountFrequency (%)
81,93
 
9.4%
82,048780492
 
6.2%
81,748780492
 
6.2%
77,421951221
 
3.1%
81,81
 
3.1%
81,648780491
 
3.1%
81,448780491
 
3.1%
81,246341461
 
3.1%
80,995121951
 
3.1%
80,695121951
 
3.1%
Other values (18)18
56.2%

Length

2022-06-24T16:30:14.134871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81,93
 
9.4%
81,748780492
 
6.2%
82,048780492
 
6.2%
79,49024391
 
3.1%
77,621951221
 
3.1%
77,724390241
 
3.1%
77,824390241
 
3.1%
77,826829271
 
3.1%
78,029268291
 
3.1%
78,18048781
 
3.1%
Other values (18)18
56.2%

Most occurring characters

ValueCountFrequency (%)
848
15.0%
438
11.8%
937
11.5%
737
11.5%
,32
10.0%
232
10.0%
128
8.7%
022
6.9%
320
6.2%
614
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number289
90.0%
Other Punctuation32
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
848
16.6%
438
13.1%
937
12.8%
737
12.8%
232
11.1%
128
9.7%
022
7.6%
320
6.9%
614
 
4.8%
513
 
4.5%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common321
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
848
15.0%
438
11.8%
937
11.5%
737
11.5%
,32
10.0%
232
10.0%
128
8.7%
022
6.9%
320
6.2%
614
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
848
15.0%
438
11.8%
937
11.5%
737
11.5%
,32
10.0%
232
10.0%
128
8.7%
022
6.9%
320
6.2%
614
 
4.4%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
69,17170732
 
1
68,88609756
 
1
73,08390244
 
1
72,66219512
 
1
72,45146341
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.9375
Min length10

Characters and Unicode

Total characters350
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row69,17170732
2nd row68,88609756
3rd row68,47439024
4th row66,87317073
5th row64,93585366

Common Values

ValueCountFrequency (%)
69,171707321
 
3.1%
68,886097561
 
3.1%
73,083902441
 
3.1%
72,662195121
 
3.1%
72,451463411
 
3.1%
71,651219511
 
3.1%
71,183414631
 
3.1%
70,743658541
 
3.1%
70,578780491
 
3.1%
70,072195121
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.235893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
69,171707321
 
3.1%
68,886097561
 
3.1%
68,474390241
 
3.1%
66,873170731
 
3.1%
64,935853661
 
3.1%
64,467073171
 
3.1%
64,690731711
 
3.1%
65,854146341
 
3.1%
66,698780491
 
3.1%
67,02975611
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
652
14.9%
740
11.4%
434
9.7%
,32
9.1%
132
9.1%
832
9.1%
532
9.1%
928
8.0%
323
6.6%
223
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number318
90.9%
Other Punctuation32
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
652
16.4%
740
12.6%
434
10.7%
132
10.1%
832
10.1%
532
10.1%
928
8.8%
323
7.2%
223
7.2%
022
6.9%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
652
14.9%
740
11.4%
434
9.7%
,32
9.1%
132
9.1%
832
9.1%
532
9.1%
928
8.0%
323
6.6%
223
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
652
14.9%
740
11.4%
434
9.7%
,32
9.1%
132
9.1%
832
9.1%
532
9.1%
928
8.0%
323
6.6%
223
6.6%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size384.0 B
77,48780488
 
2
78,53902439
 
2
78,74146341
 
2
75,01707317
 
1
78,78780488
 
1
Other values (24)
24 

Length

Max length11
Median length11
Mean length10.8125
Min length10

Characters and Unicode

Total characters346
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)81.2%

Sample

1st row75,01707317
2nd row75,21463415
3rd row75,36585366
4th row75,61707317
5th row75,4195122

Common Values

ValueCountFrequency (%)
77,487804882
 
6.2%
78,539024392
 
6.2%
78,741463412
 
6.2%
75,017073171
 
3.1%
78,787804881
 
3.1%
78,639024391
 
3.1%
78,69024391
 
3.1%
78,841463411
 
3.1%
78,641463411
 
3.1%
78,541463411
 
3.1%
Other values (19)19
59.4%

Length

2022-06-24T16:30:14.333915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
77,487804882
 
6.2%
78,741463412
 
6.2%
78,539024392
 
6.2%
76,936585371
 
3.1%
75,365853661
 
3.1%
75,617073171
 
3.1%
75,41951221
 
3.1%
75,61951221
 
3.1%
75,621951221
 
3.1%
76,026829271
 
3.1%
Other values (19)19
59.4%

Most occurring characters

ValueCountFrequency (%)
759
17.1%
849
14.2%
434
9.8%
,32
9.2%
631
9.0%
330
8.7%
526
7.5%
223
 
6.6%
122
 
6.4%
921
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number314
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
759
18.8%
849
15.6%
434
10.8%
631
9.9%
330
9.6%
526
8.3%
223
 
7.3%
122
 
7.0%
921
 
6.7%
019
 
6.1%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
759
17.1%
849
14.2%
434
9.8%
,32
9.2%
631
9.0%
330
8.7%
526
7.5%
223
 
6.6%
122
 
6.4%
921
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
759
17.1%
849
14.2%
434
9.8%
,32
9.2%
631
9.0%
330
8.7%
526
7.5%
223
 
6.6%
122
 
6.4%
921
 
6.1%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size384.0 B
81,25609756
 
2
75,58292683
 
1
79,04878049
 
1
81,20487805
 
1
81,15609756
 
1
Other values (26)
26 

Length

Max length11
Median length11
Mean length10.6875
Min length4

Characters and Unicode

Total characters342
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)93.8%

Sample

1st row75,58292683
2nd row75,8804878
3rd row76,08292683
4th row76,43414634
5th row76,38536585

Common Values

ValueCountFrequency (%)
81,256097562
 
6.2%
75,582926831
 
3.1%
79,048780491
 
3.1%
81,204878051
 
3.1%
81,156097561
 
3.1%
80,956097561
 
3.1%
81,304878051
 
3.1%
81,004878051
 
3.1%
80,904878051
 
3.1%
80,951219511
 
3.1%
Other values (21)21
65.6%

Length

2022-06-24T16:30:14.436939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81,256097562
 
6.2%
75,88048781
 
3.1%
76,082926831
 
3.1%
76,434146341
 
3.1%
76,385365851
 
3.1%
76,885365851
 
3.1%
76,836585371
 
3.1%
77,087804881
 
3.1%
77,210975611
 
3.1%
77,19024391
 
3.1%
Other values (21)21
65.6%

Most occurring characters

ValueCountFrequency (%)
849
14.3%
743
12.6%
036
10.5%
435
10.2%
,32
9.4%
930
8.8%
528
8.2%
627
7.9%
122
6.4%
220
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number310
90.6%
Other Punctuation32
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
849
15.8%
743
13.9%
036
11.6%
435
11.3%
930
9.7%
528
9.0%
627
8.7%
122
7.1%
220
6.5%
320
6.5%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
849
14.3%
743
12.6%
036
10.5%
435
10.2%
,32
9.4%
930
8.8%
528
8.2%
627
7.9%
122
6.4%
220
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
849
14.3%
743
12.6%
036
10.5%
435
10.2%
,32
9.4%
930
8.8%
528
8.2%
627
7.9%
122
6.4%
220
5.8%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
65,947
 
1
66,343
 
1
75,881
 
1
75,672
 
1
75,456
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.84375
Min length4

Characters and Unicode

Total characters187
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row65,947
2nd row66,343
3rd row66,742
4th row67,141
5th row67,539

Common Values

ValueCountFrequency (%)
65,9471
 
3.1%
66,3431
 
3.1%
75,8811
 
3.1%
75,6721
 
3.1%
75,4561
 
3.1%
75,231
 
3.1%
74,9941
 
3.1%
74,7451
 
3.1%
74,4831
 
3.1%
74,2091
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.539962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65,9471
 
3.1%
66,3431
 
3.1%
66,7421
 
3.1%
67,1411
 
3.1%
67,5391
 
3.1%
67,9321
 
3.1%
68,3181
 
3.1%
68,6951
 
3.1%
69,0611
 
3.1%
69,4191
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
,32
17.1%
730
16.0%
627
14.4%
117
9.1%
916
8.6%
415
8.0%
313
7.0%
211
 
5.9%
510
 
5.3%
810
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number155
82.9%
Other Punctuation32
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
730
19.4%
627
17.4%
117
11.0%
916
10.3%
415
9.7%
313
8.4%
211
 
7.1%
510
 
6.5%
810
 
6.5%
06
 
3.9%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,32
17.1%
730
16.0%
627
14.4%
117
9.1%
916
8.6%
415
8.0%
313
7.0%
211
 
5.9%
510
 
5.3%
810
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,32
17.1%
730
16.0%
627
14.4%
117
9.1%
916
8.6%
415
8.0%
313
7.0%
211
 
5.9%
510
 
5.3%
810
 
5.3%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
57,4
 
1
57,865
 
1
69,656
 
1
69,416
 
1
69,165
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.75
Min length4

Characters and Unicode

Total characters184
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row57,4
2nd row57,865
3rd row58,353
4th row58,851
5th row59,349

Common Values

ValueCountFrequency (%)
57,41
 
3.1%
57,8651
 
3.1%
69,6561
 
3.1%
69,4161
 
3.1%
69,1651
 
3.1%
68,8971
 
3.1%
68,6071
 
3.1%
68,2861
 
3.1%
67,9311
 
3.1%
67,5451
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.642984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
57,41
 
3.1%
57,8651
 
3.1%
58,3531
 
3.1%
58,8511
 
3.1%
59,3491
 
3.1%
59,841
 
3.1%
60,321
 
3.1%
60,7831
 
3.1%
61,2331
 
3.1%
61,6691
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
639
21.2%
,32
17.4%
520
10.9%
918
9.8%
315
 
8.2%
814
 
7.6%
412
 
6.5%
711
 
6.0%
18
 
4.3%
08
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number152
82.6%
Other Punctuation32
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
639
25.7%
520
13.2%
918
11.8%
315
 
9.9%
814
 
9.2%
412
 
7.9%
711
 
7.2%
18
 
5.3%
08
 
5.3%
27
 
4.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
639
21.2%
,32
17.4%
520
10.9%
918
9.8%
315
 
8.2%
814
 
7.6%
412
 
6.5%
711
 
6.0%
18
 
4.3%
08
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
639
21.2%
,32
17.4%
520
10.9%
918
9.8%
315
 
8.2%
814
 
7.6%
412
 
6.5%
711
 
6.0%
18
 
4.3%
08
 
4.3%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
70,469
 
1
70,866
 
1
75,054
 
1
74,992
 
1
74,947
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.84375
Min length4

Characters and Unicode

Total characters187
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row70,469
2nd row70,866
3rd row71,248
4th row71,61
5th row71,953

Common Values

ValueCountFrequency (%)
70,4691
 
3.1%
70,8661
 
3.1%
75,0541
 
3.1%
74,9921
 
3.1%
74,9471
 
3.1%
74,9171
 
3.1%
74,9041
 
3.1%
74,9081
 
3.1%
74,931
 
3.1%
74,9661
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.750009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
70,4691
 
3.1%
70,8661
 
3.1%
71,2481
 
3.1%
71,611
 
3.1%
71,9531
 
3.1%
72,2791
 
3.1%
72,5981
 
3.1%
72,9251
 
3.1%
73,2681
 
3.1%
73,6251
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
735
18.7%
,32
17.1%
520
10.7%
418
9.6%
917
9.1%
215
8.0%
113
 
7.0%
611
 
5.9%
810
 
5.3%
39
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number155
82.9%
Other Punctuation32
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
735
22.6%
520
12.9%
418
11.6%
917
11.0%
215
9.7%
113
 
8.4%
611
 
7.1%
810
 
6.5%
39
 
5.8%
07
 
4.5%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
735
18.7%
,32
17.1%
520
10.7%
418
9.6%
917
9.1%
215
8.0%
113
 
7.0%
611
 
5.9%
810
 
5.3%
39
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
735
18.7%
,32
17.1%
520
10.7%
418
9.6%
917
9.1%
215
8.0%
113
 
7.0%
611
 
5.9%
810
 
5.3%
39
 
4.8%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
63,035
 
1
63,307
 
1
64,131
 
1
63,857
 
1
63,538
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.875
Min length5

Characters and Unicode

Total characters188
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row63,035
2nd row63,307
3rd row63,384
4th row63,247
5th row62,894

Common Values

ValueCountFrequency (%)
63,0351
 
3.1%
63,3071
 
3.1%
64,1311
 
3.1%
63,8571
 
3.1%
63,5381
 
3.1%
63,1531
 
3.1%
62,6491
 
3.1%
61,9681
 
3.1%
61,0991
 
3.1%
60,061
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.853031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63,0351
 
3.1%
63,3071
 
3.1%
63,3841
 
3.1%
63,2471
 
3.1%
62,8941
 
3.1%
62,3311
 
3.1%
61,5611
 
3.1%
60,5951
 
3.1%
59,4891
 
3.1%
58,3151
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
,32
17.0%
528
14.9%
627
14.4%
323
12.2%
420
10.6%
914
7.4%
111
 
5.9%
811
 
5.9%
79
 
4.8%
08
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156
83.0%
Other Punctuation32
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
528
17.9%
627
17.3%
323
14.7%
420
12.8%
914
9.0%
111
 
7.1%
811
 
7.1%
79
 
5.8%
08
 
5.1%
25
 
3.2%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common188
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,32
17.0%
528
14.9%
627
14.4%
323
12.2%
420
10.6%
914
7.4%
111
 
5.9%
811
 
5.9%
79
 
4.8%
08
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,32
17.0%
528
14.9%
627
14.4%
323
12.2%
420
10.6%
914
7.4%
111
 
5.9%
811
 
5.9%
79
 
4.8%
08
 
4.3%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
69,054
 
1
69,145
 
1
76,912
 
1
76,704
 
1
76,47
 
1
Other values (27)
27 

Length

Max length6
Median length6
Mean length5.875
Min length5

Characters and Unicode

Total characters188
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row69,054
2nd row69,145
3rd row69,242
4th row69,355
5th row69,496

Common Values

ValueCountFrequency (%)
69,0541
 
3.1%
69,1451
 
3.1%
76,9121
 
3.1%
76,7041
 
3.1%
76,471
 
3.1%
76,211
 
3.1%
75,9281
 
3.1%
75,6291
 
3.1%
75,3211
 
3.1%
75,0131
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:14.950053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
69,0541
 
3.1%
69,1451
 
3.1%
69,2421
 
3.1%
69,3551
 
3.1%
69,4961
 
3.1%
69,671
 
3.1%
69,8851
 
3.1%
70,141
 
3.1%
70,4281
 
3.1%
70,7371
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
736
19.1%
,32
17.0%
617
9.0%
917
9.0%
114
 
7.4%
214
 
7.4%
513
 
6.9%
313
 
6.9%
412
 
6.4%
011
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156
83.0%
Other Punctuation32
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
736
23.1%
617
10.9%
917
10.9%
114
 
9.0%
214
 
9.0%
513
 
8.3%
313
 
8.3%
412
 
7.7%
011
 
7.1%
89
 
5.8%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common188
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
736
19.1%
,32
17.0%
617
9.0%
917
9.0%
114
 
7.4%
214
 
7.4%
513
 
6.9%
313
 
6.9%
412
 
6.4%
011
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
736
19.1%
,32
17.0%
617
9.0%
917
9.0%
114
 
7.4%
214
 
7.4%
513
 
6.9%
313
 
6.9%
412
 
6.4%
011
 
5.9%

Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
65,24704931
 
1
65,43323283
 
1
72,76266676
 
1
72,57398037
 
1
72,39140625
 
1
Other values (27)
27 

Length

Max length11
Median length11
Mean length10.875
Min length10

Characters and Unicode

Total characters348
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row65,24704931
2nd row65,43323283
3rd row65,61838085
4th row65,77001658
5th row65,8844036

Common Values

ValueCountFrequency (%)
65,247049311
 
3.1%
65,433232831
 
3.1%
72,762666761
 
3.1%
72,573980371
 
3.1%
72,391406251
 
3.1%
72,186100651
 
3.1%
71,952114721
 
3.1%
71,746054731
 
3.1%
71,465864411
 
3.1%
71,173298991
 
3.1%
Other values (22)22
68.8%

Length

2022-06-24T16:30:15.049075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65,247049311
 
3.1%
65,433232831
 
3.1%
65,618380851
 
3.1%
65,770016581
 
3.1%
65,88440361
 
3.1%
66,087772341
 
3.1%
66,274217261
 
3.1%
66,558517521
 
3.1%
66,843390021
 
3.1%
67,086499211
 
3.1%
Other values (22)22
68.8%

Most occurring characters

ValueCountFrequency (%)
648
13.8%
739
11.2%
233
9.5%
,32
9.2%
831
8.9%
030
8.6%
129
8.3%
528
8.0%
428
8.0%
926
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number316
90.8%
Other Punctuation32
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
648
15.2%
739
12.3%
233
10.4%
831
9.8%
030
9.5%
129
9.2%
528
8.9%
428
8.9%
926
8.2%
324
7.6%
Other Punctuation
ValueCountFrequency (%)
,32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
648
13.8%
739
11.2%
233
9.5%
,32
9.2%
831
8.9%
030
8.6%
129
8.3%
528
8.0%
428
8.0%
926
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
648
13.8%
739
11.2%
233
9.5%
,32
9.2%
831
8.9%
030
8.6%
129
8.3%
528
8.0%
428
8.0%
926
7.5%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
4421,364531
 
1
3817,549415
 
1
3779,461921
 
1
3939,529563
 
1
3876,948104
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.85185185
Min length10

Characters and Unicode

Total characters293
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row4514,704706
2nd row4421,364531
3rd row4302,891175
4th row4190,98422
5th row4122,69574

Common Values

ValueCountFrequency (%)
4421,3645311
 
3.1%
3817,5494151
 
3.1%
3779,4619211
 
3.1%
3939,5295631
 
3.1%
3876,9481041
 
3.1%
3869,8162291
 
3.1%
3997,0794211
 
3.1%
3790,5011521
 
3.1%
4036,8307811
 
3.1%
3985,8119551
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.146097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4421,3645311
 
3.7%
4108,4012991
 
3.7%
4302,8911751
 
3.7%
4190,984221
 
3.7%
4122,695741
 
3.7%
4088,798131
 
3.7%
4119,6889481
 
3.7%
4246,5531911
 
3.7%
4203,3573671
 
3.7%
4177,2941291
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
444
15.0%
935
11.9%
130
10.2%
328
9.6%
,27
9.2%
026
8.9%
824
8.2%
721
7.2%
220
6.8%
619
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number266
90.8%
Other Punctuation27
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
444
16.5%
935
13.2%
130
11.3%
328
10.5%
026
9.8%
824
9.0%
721
7.9%
220
7.5%
619
7.1%
519
7.1%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
444
15.0%
935
11.9%
130
10.2%
328
9.6%
,27
9.2%
026
8.9%
824
8.2%
721
7.2%
220
6.8%
619
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
444
15.0%
935
11.9%
130
10.2%
328
9.6%
,27
9.2%
026
8.9%
824
8.2%
721
7.2%
220
6.8%
619
6.5%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
3846,625257
 
1
3692,017833
 
1
3659,087795
 
1
3833,534259
 
1
3836,656299
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.81481481
Min length9

Characters and Unicode

Total characters292
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row3795,900235
2nd row3846,625257
3rd row4043,09916
4th row3954,003261
5th row4001,902202

Common Values

ValueCountFrequency (%)
3846,6252571
 
3.1%
3692,0178331
 
3.1%
3659,0877951
 
3.1%
3833,5342591
 
3.1%
3836,6562991
 
3.1%
3847,07221
 
3.1%
4016,8480711
 
3.1%
3913,4579421
 
3.1%
4110,5859781
 
3.1%
4115,527241
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.250120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3846,6252571
 
3.7%
4225,4680371
 
3.7%
4043,099161
 
3.7%
3954,0032611
 
3.7%
4001,9022021
 
3.7%
3836,1118341
 
3.7%
3981,3521751
 
3.7%
4191,9241841
 
3.7%
4049,2331831
 
3.7%
4151,627471
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
439
13.4%
336
12.3%
128
9.6%
,27
9.2%
227
9.2%
824
8.2%
524
8.2%
024
8.2%
923
7.9%
722
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number265
90.8%
Other Punctuation27
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
439
14.7%
336
13.6%
128
10.6%
227
10.2%
824
9.1%
524
9.1%
024
9.1%
923
8.7%
722
8.3%
618
6.8%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
439
13.4%
336
12.3%
128
9.6%
,27
9.2%
227
9.2%
824
8.2%
524
8.2%
024
8.2%
923
7.9%
722
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
439
13.4%
336
12.3%
128
9.6%
,27
9.2%
227
9.2%
824
8.2%
524
8.2%
024
8.2%
923
7.9%
722
7.5%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
2583,888077
 
1
2481,754645
 
1
2414,484002
 
1
2579,472543
 
1
2709,297728
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.88888889
Min length8

Characters and Unicode

Total characters294
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row2566,108925
2nd row2583,888077
3rd row2645,673237
4th row2627,340008
5th row2611,990438

Common Values

ValueCountFrequency (%)
2583,8880771
 
3.1%
2481,7546451
 
3.1%
2414,4840021
 
3.1%
2579,4725431
 
3.1%
2709,2977281
 
3.1%
2828,4048911
 
3.1%
2930,5885241
 
3.1%
2869,9207121
 
3.1%
3087,5663311
 
3.1%
3149,5765531
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.352143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2583,8880771
 
3.7%
3037,2669651
 
3.7%
2645,6732371
 
3.7%
2627,3400081
 
3.7%
2611,9904381
 
3.7%
2578,4659781
 
3.7%
2799,3757621
 
3.7%
2796,1547361
 
3.7%
2834,4029461
 
3.7%
2912,9473651
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
242
14.3%
728
9.5%
,27
9.2%
927
9.2%
326
8.8%
426
8.8%
626
8.8%
525
8.5%
125
8.5%
823
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number267
90.8%
Other Punctuation27
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
242
15.7%
728
10.5%
927
10.1%
326
9.7%
426
9.7%
626
9.7%
525
9.4%
125
9.4%
823
8.6%
019
7.1%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common294
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
242
14.3%
728
9.5%
,27
9.2%
927
9.2%
326
8.8%
426
8.8%
626
8.8%
525
8.5%
125
8.5%
823
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
242
14.3%
728
9.5%
,27
9.2%
927
9.2%
326
8.8%
426
8.8%
626
8.8%
525
8.5%
125
8.5%
823
7.8%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
3552,858582
 
1
3428,557216
 
1
3470,763129
 
1
3567,629354
 
1
3537,36317
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.85185185
Min length10

Characters and Unicode

Total characters293
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row3352,005932
2nd row3552,858582
3rd row3574,005929
4th row3644,19476
5th row3655,966843

Common Values

ValueCountFrequency (%)
3552,8585821
 
3.1%
3428,5572161
 
3.1%
3470,7631291
 
3.1%
3567,6293541
 
3.1%
3537,363171
 
3.1%
3610,8121691
 
3.1%
3893,2666041
 
3.1%
3678,5111331
 
3.1%
3858,4345211
 
3.1%
4012,6542451
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.451165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3552,8585821
 
3.7%
3991,9775671
 
3.7%
3574,0059291
 
3.7%
3644,194761
 
3.7%
3655,9668431
 
3.7%
3844,286681
 
3.7%
3934,9595611
 
3.7%
4008,4762361
 
3.7%
4041,167671
 
3.7%
3955,6276271
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
339
13.3%
432
10.9%
630
10.2%
529
9.9%
,27
9.2%
225
8.5%
823
7.8%
723
7.8%
022
7.5%
922
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number266
90.8%
Other Punctuation27
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
339
14.7%
432
12.0%
630
11.3%
529
10.9%
225
9.4%
823
8.6%
723
8.6%
022
8.3%
922
8.3%
121
7.9%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
339
13.3%
432
10.9%
630
10.2%
529
9.9%
,27
9.2%
225
8.5%
823
7.8%
723
7.8%
022
7.5%
922
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
339
13.3%
432
10.9%
630
10.2%
529
9.9%
,27
9.2%
225
8.5%
823
7.8%
723
7.8%
022
7.5%
922
7.5%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
7630,092306
 
1
7631,34157
 
1
7897,855615
 
1
7743,725742
 
1
7733,411655
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.96296296
Min length10

Characters and Unicode

Total characters296
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row7963,297905
2nd row7630,092306
3rd row7517,330304
4th row7601,685177
5th row7770,546448

Common Values

ValueCountFrequency (%)
7630,0923061
 
3.1%
7631,341571
 
3.1%
7897,8556151
 
3.1%
7743,7257421
 
3.1%
7733,4116551
 
3.1%
7911,5545881
 
3.1%
7788,5607861
 
3.1%
7797,1211361
 
3.1%
8194,8807711
 
3.1%
8213,3895431
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.549187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7630,0923061
 
3.7%
7993,8793841
 
3.7%
7517,3303041
 
3.7%
7601,6851771
 
3.7%
7770,5464481
 
3.7%
7957,2456331
 
3.7%
7979,8984461
 
3.7%
8053,4548581
 
3.7%
8056,2436281
 
3.7%
7948,3972781
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
743
14.5%
833
11.1%
532
10.8%
431
10.5%
330
10.1%
,27
9.1%
923
7.8%
123
7.8%
620
6.8%
018
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number269
90.9%
Other Punctuation27
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
743
16.0%
833
12.3%
532
11.9%
431
11.5%
330
11.2%
923
8.6%
123
8.6%
620
7.4%
018
6.7%
216
 
5.9%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
743
14.5%
833
11.1%
532
10.8%
431
10.5%
330
10.1%
,27
9.1%
923
7.8%
123
7.8%
620
6.8%
018
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
743
14.5%
833
11.1%
532
10.8%
431
10.5%
330
10.1%
,27
9.1%
923
7.8%
123
7.8%
620
6.8%
018
6.1%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct25
Distinct (%)100.0%
Missing7
Missing (%)21.9%
Memory size384.0 B
5941,586256
 
1
5078,626342
 
1
5167,010353
 
1
5049,426631
 
1
4819,040782
 
1
Other values (20)
20 

Length

Max length11
Median length11
Mean length10.92
Min length10

Characters and Unicode

Total characters273
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row5941,586256
2nd row5870,233857
3rd row5356,50974
4th row5059,864625
5th row4426,668195

Common Values

ValueCountFrequency (%)
5941,5862561
 
3.1%
5078,6263421
 
3.1%
5167,0103531
 
3.1%
5049,4266311
 
3.1%
4819,0407821
 
3.1%
4531,2864631
 
3.1%
4823,1259951
 
3.1%
4709,8448871
 
3.1%
4688,3910231
 
3.1%
4540,9081561
 
3.1%
Other values (15)15
46.9%
(Missing)7
21.9%

Length

2022-06-24T16:30:15.646209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5941,5862561
 
4.0%
4288,3826311
 
4.0%
5870,2338571
 
4.0%
5356,509741
 
4.0%
5059,8646251
 
4.0%
4426,6681951
 
4.0%
4290,6934671
 
4.0%
4252,6116191
 
4.0%
4069,6872921
 
4.0%
3981,4994681
 
4.0%
Other values (15)15
60.0%

Most occurring characters

ValueCountFrequency (%)
439
14.3%
829
10.6%
628
10.3%
527
9.9%
226
9.5%
,25
9.2%
923
8.4%
123
8.4%
322
8.1%
016
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number248
90.8%
Other Punctuation25
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
439
15.7%
829
11.7%
628
11.3%
527
10.9%
226
10.5%
923
9.3%
123
9.3%
322
8.9%
016
6.5%
715
 
6.0%
Other Punctuation
ValueCountFrequency (%)
,25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common273
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
439
14.3%
829
10.6%
628
10.3%
527
9.9%
226
9.5%
,25
9.2%
923
8.4%
123
8.4%
322
8.1%
016
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
439
14.3%
829
10.6%
628
10.3%
527
9.9%
226
9.5%
,25
9.2%
923
8.4%
123
8.4%
322
8.1%
016
5.9%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
7671,773066
 
1
6803,996607
 
1
6960,683997
 
1
6905,598633
 
1
6872,027284
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.92592593
Min length10

Characters and Unicode

Total characters295
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row7890,287202
2nd row7671,773066
3rd row7631,467806
4th row7677,401401
5th row7709,496589

Common Values

ValueCountFrequency (%)
7671,7730661
 
3.1%
6803,9966071
 
3.1%
6960,6839971
 
3.1%
6905,5986331
 
3.1%
6872,0272841
 
3.1%
7029,9546011
 
3.1%
7161,4265521
 
3.1%
7056,7836531
 
3.1%
7488,0819211
 
3.1%
7758,1659861
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.878261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7671,7730661
 
3.7%
7843,3448491
 
3.7%
7631,4678061
 
3.7%
7677,4014011
 
3.7%
7709,4965891
 
3.7%
7757,8308221
 
3.7%
7763,7551061
 
3.7%
7844,4682661
 
3.7%
7828,5810961
 
3.7%
7803,6976051
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
745
15.3%
640
13.6%
838
12.9%
,27
9.2%
924
8.1%
524
8.1%
022
7.5%
222
7.5%
321
7.1%
419
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number268
90.8%
Other Punctuation27
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
745
16.8%
640
14.9%
838
14.2%
924
9.0%
524
9.0%
022
8.2%
222
8.2%
321
7.8%
419
7.1%
113
 
4.9%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common295
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
745
15.3%
640
13.6%
838
12.9%
,27
9.2%
924
8.1%
524
8.1%
022
7.5%
222
7.5%
321
7.1%
419
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
745
15.3%
640
13.6%
838
12.9%
,27
9.2%
924
8.1%
524
8.1%
022
7.5%
222
7.5%
321
7.1%
419
6.4%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
3597,051621
 
1
2764,516671
 
1
2777,310987
 
1
2987,700589
 
1
3042,859871
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters297
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row3626,297861
2nd row3597,051621
3rd row3707,951971
4th row3684,724535
5th row3713,185819

Common Values

ValueCountFrequency (%)
3597,0516211
 
3.1%
2764,5166711
 
3.1%
2777,3109871
 
3.1%
2987,7005891
 
3.1%
3042,8598711
 
3.1%
2972,1530651
 
3.1%
3230,6159841
 
3.1%
3145,5856621
 
3.1%
3361,9805171
 
3.1%
3441,6402191
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:15.976283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3597,0516211
 
3.7%
3685,8694871
 
3.7%
3707,9519711
 
3.7%
3684,7245351
 
3.7%
3713,1858191
 
3.7%
3732,5211141
 
3.7%
3728,9631521
 
3.7%
3879,8227861
 
3.7%
3760,7123651
 
3.7%
3787,1084521
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
736
12.1%
335
11.8%
832
10.8%
130
10.1%
528
9.4%
,27
9.1%
626
8.8%
923
7.7%
223
7.7%
419
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number270
90.9%
Other Punctuation27
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
736
13.3%
335
13.0%
832
11.9%
130
11.1%
528
10.4%
626
9.6%
923
8.5%
223
8.5%
419
7.0%
018
6.7%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
736
12.1%
335
11.8%
832
10.8%
130
10.1%
528
9.4%
,27
9.1%
626
8.8%
923
7.7%
223
7.7%
419
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
736
12.1%
335
11.8%
832
10.8%
130
10.1%
528
9.4%
,27
9.1%
626
8.8%
923
7.7%
223
7.7%
419
6.4%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct26
Distinct (%)100.0%
Missing6
Missing (%)18.8%
Memory size384.0 B
1094,424682
 
1
1495,541141
 
1
1461,076774
 
1
1413,733385
 
1
1367,188049
 
1
Other values (21)
21 

Length

Max length11
Median length11
Mean length10.96153846
Min length10

Characters and Unicode

Total characters285
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row993,9052667
2nd row940,9658549
3rd row942,0380381
4th row935,7464624
5th row943,3269952

Common Values

ValueCountFrequency (%)
1094,4246821
 
3.1%
1495,5411411
 
3.1%
1461,0767741
 
3.1%
1413,7333851
 
3.1%
1367,1880491
 
3.1%
1358,5024021
 
3.1%
1240,1773041
 
3.1%
1294,4808331
 
3.1%
1238,4116281
 
3.1%
1184,1451211
 
3.1%
Other values (16)16
50.0%
(Missing)6
 
18.8%

Length

2022-06-24T16:30:16.072304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1094,4246821
 
3.8%
1495,5411411
 
3.8%
942,03803811
 
3.8%
935,74646241
 
3.8%
943,32699521
 
3.8%
976,98217741
 
3.8%
994,28984921
 
3.8%
1030,6680721
 
3.8%
1066,1869511
 
3.8%
1075,1990811
 
3.8%
Other values (16)16
61.5%

Most occurring characters

ValueCountFrequency (%)
144
15.4%
437
13.0%
931
10.9%
,26
9.1%
025
8.8%
823
8.1%
322
7.7%
221
7.4%
720
7.0%
619
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number259
90.9%
Other Punctuation26
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
144
17.0%
437
14.3%
931
12.0%
025
9.7%
823
8.9%
322
8.5%
221
8.1%
720
7.7%
619
7.3%
517
 
6.6%
Other Punctuation
ValueCountFrequency (%)
,26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
144
15.4%
437
13.0%
931
10.9%
,26
9.1%
025
8.8%
823
8.1%
322
7.7%
221
7.4%
720
7.0%
619
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
144
15.4%
437
13.0%
931
10.9%
,26
9.1%
025
8.8%
823
8.1%
322
7.7%
221
7.4%
720
7.0%
619
6.7%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct26
Distinct (%)100.0%
Missing6
Missing (%)18.8%
Memory size384.0 B
424,2943448
 
1
636,571834
 
1
605,7940378
 
1
599,1556198
 
1
577,9944263
 
1
Other values (21)
21 

Length

Max length11
Median length11
Mean length10.80769231
Min length10

Characters and Unicode

Total characters281
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row342,700996
2nd row350,0757335
3rd row357,3667628
4th row363,0783448
5th row364,5144994

Common Values

ValueCountFrequency (%)
424,29434481
 
3.1%
636,5718341
 
3.1%
605,79403781
 
3.1%
599,15561981
 
3.1%
577,99442631
 
3.1%
561,65340591
 
3.1%
544,62659731
 
3.1%
501,55963621
 
3.1%
485,09962871
 
3.1%
466,13826111
 
3.1%
Other values (16)16
50.0%
(Missing)6
 
18.8%

Length

2022-06-24T16:30:16.173327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
424,29434481
 
3.8%
636,5718341
 
3.8%
357,36676281
 
3.8%
363,07834481
 
3.8%
364,51449941
 
3.8%
371,22591411
 
3.8%
385,09185741
 
3.8%
389,4265161
 
3.8%
397,37818551
 
3.8%
399,49882921
 
3.8%
Other values (16)16
61.5%

Most occurring characters

ValueCountFrequency (%)
439
13.9%
930
10.7%
530
10.7%
327
9.6%
627
9.6%
,26
9.3%
723
8.2%
822
7.8%
120
7.1%
219
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number255
90.7%
Other Punctuation26
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
439
15.3%
930
11.8%
530
11.8%
327
10.6%
627
10.6%
723
9.0%
822
8.6%
120
7.8%
219
7.5%
018
7.1%
Other Punctuation
ValueCountFrequency (%)
,26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
439
13.9%
930
10.7%
530
10.7%
327
9.6%
627
9.6%
,26
9.3%
723
8.2%
822
7.8%
120
7.1%
219
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
439
13.9%
930
10.7%
530
10.7%
327
9.6%
627
9.6%
,26
9.3%
723
8.2%
822
7.8%
120
7.1%
219
6.8%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct27
Distinct (%)100.0%
Missing5
Missing (%)15.6%
Memory size384.0 B
1473,38861
 
1
1537,261412
 
1
1561,873342
 
1
1616,613645
 
1
1634,697418
 
1
Other values (22)
22 

Length

Max length11
Median length11
Mean length10.88888889
Min length10

Characters and Unicode

Total characters294
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row1426,671258
2nd row1473,38861
3rd row1518,489277
4th row1503,678122
5th row1502,215639

Common Values

ValueCountFrequency (%)
1473,388611
 
3.1%
1537,2614121
 
3.1%
1561,8733421
 
3.1%
1616,6136451
 
3.1%
1634,6974181
 
3.1%
1587,0720711
 
3.1%
1531,7569681
 
3.1%
1599,5167111
 
3.1%
1621,2649641
 
3.1%
1662,602581
 
3.1%
Other values (17)17
53.1%
(Missing)5
 
15.6%

Length

2022-06-24T16:30:16.275350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1473,388611
 
3.7%
1534,721761
 
3.7%
1518,4892771
 
3.7%
1503,6781221
 
3.7%
1502,2156391
 
3.7%
1509,9393171
 
3.7%
1437,6468811
 
3.7%
1445,9336531
 
3.7%
1498,0018821
 
3.7%
1516,6108341
 
3.7%
Other values (17)17
63.0%

Most occurring characters

ValueCountFrequency (%)
156
19.0%
636
12.2%
533
11.2%
,27
9.2%
325
8.5%
723
7.8%
823
7.8%
222
 
7.5%
420
 
6.8%
918
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number267
90.8%
Other Punctuation27
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
156
21.0%
636
13.5%
533
12.4%
325
9.4%
723
8.6%
823
8.6%
222
 
8.2%
420
 
7.5%
918
 
6.7%
011
 
4.1%
Other Punctuation
ValueCountFrequency (%)
,27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common294
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
156
19.0%
636
12.2%
533
11.2%
,27
9.2%
325
8.5%
723
7.8%
823
7.8%
222
 
7.5%
420
 
6.8%
918
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
19.0%
636
12.2%
533
11.2%
,27
9.2%
325
8.5%
723
7.8%
823
7.8%
222
 
7.5%
420
 
6.8%
918
 
6.1%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct26
Distinct (%)100.0%
Missing6
Missing (%)18.8%
Memory size384.0 B
2518,332793
 
1
2695,505776
 
1
2602,845598
 
1
2636,684726
 
1
2716,681173
 
1
Other values (21)
21 

Length

Max length11
Median length11
Mean length10.92307692
Min length10

Characters and Unicode

Total characters284
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row2585,623273
2nd row2471,598122
3rd row2518,117842
4th row2290,666487
5th row2395,381195

Common Values

ValueCountFrequency (%)
2518,3327931
 
3.1%
2695,5057761
 
3.1%
2602,8455981
 
3.1%
2636,6847261
 
3.1%
2716,6811731
 
3.1%
2768,0945071
 
3.1%
2852,0954481
 
3.1%
2950,153611
 
3.1%
2775,6152581
 
3.1%
2625,7947911
 
3.1%
Other values (16)16
50.0%
(Missing)6
 
18.8%

Length

2022-06-24T16:30:16.373372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2518,3327931
 
3.8%
2695,5057761
 
3.8%
2518,1178421
 
3.8%
2290,6664871
 
3.8%
2395,3811951
 
3.8%
2420,0831121
 
3.8%
2498,8936251
 
3.8%
2504,9031151
 
3.8%
2527,9401741
 
3.8%
2471,4770361
 
3.8%
Other values (16)16
61.5%

Most occurring characters

ValueCountFrequency (%)
250
17.6%
527
9.5%
127
9.5%
,26
9.2%
726
9.2%
626
9.2%
426
9.2%
823
8.1%
320
 
7.0%
918
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number258
90.8%
Other Punctuation26
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
250
19.4%
527
10.5%
127
10.5%
726
10.1%
626
10.1%
426
10.1%
823
8.9%
320
 
7.8%
918
 
7.0%
015
 
5.8%
Other Punctuation
ValueCountFrequency (%)
,26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
250
17.6%
527
9.5%
127
9.5%
,26
9.2%
726
9.2%
626
9.2%
426
9.2%
823
8.1%
320
 
7.0%
918
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
250
17.6%
527
9.5%
127
9.5%
,26
9.2%
726
9.2%
626
9.2%
426
9.2%
823
8.1%
320
 
7.0%
918
 
6.3%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct26
Distinct (%)100.0%
Missing6
Missing (%)18.8%
Memory size384.0 B
1118,431773
 
1
2224,354898
 
1
2204,243299
 
1
2149,602569
 
1
2085,083022
 
1
Other values (21)
21 

Length

Max length11
Median length11
Mean length10.88461538
Min length9

Characters and Unicode

Total characters283
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row724,1161051
2nd row766,9953294
3rd row736,8518014
4th row752,6286625
5th row788,1287236

Common Values

ValueCountFrequency (%)
1118,4317731
 
3.1%
2224,3548981
 
3.1%
2204,2432991
 
3.1%
2149,6025691
 
3.1%
2085,0830221
 
3.1%
1954,7225561
 
3.1%
1778,4335191
 
3.1%
1672,904121
 
3.1%
1630,1710291
 
3.1%
1515,1736781
 
3.1%
Other values (16)16
50.0%
(Missing)6
 
18.8%

Length

2022-06-24T16:30:16.474394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1118,4317731
 
3.8%
2224,3548981
 
3.8%
736,85180141
 
3.8%
752,62866251
 
3.8%
788,12872361
 
3.8%
816,162891
 
3.8%
866,83437431
 
3.8%
881,65373741
 
3.8%
871,75632381
 
3.8%
869,35860731
 
3.8%
Other values (16)16
61.5%

Most occurring characters

ValueCountFrequency (%)
137
13.1%
833
11.7%
231
11.0%
330
10.6%
,26
9.2%
725
8.8%
625
8.8%
421
7.4%
521
7.4%
921
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number257
90.8%
Other Punctuation26
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
137
14.4%
833
12.8%
231
12.1%
330
11.7%
725
9.7%
625
9.7%
421
8.2%
521
8.2%
921
8.2%
013
 
5.1%
Other Punctuation
ValueCountFrequency (%)
,26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
137
13.1%
833
11.7%
231
11.0%
330
10.6%
,26
9.2%
725
8.8%
625
8.8%
421
7.4%
521
7.4%
921
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
137
13.1%
833
11.7%
231
11.0%
330
10.6%
,26
9.2%
725
8.8%
625
8.8%
421
7.4%
521
7.4%
921
7.4%

Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct26
Distinct (%)100.0%
Missing6
Missing (%)18.8%
Memory size384.0 B
1688,480279
 
1
1919,991765
 
1
1894,112059
 
1
1891,700426
 
1
1881,477548
 
1
Other values (21)
21 

Length

Max length11
Median length11
Mean length10.96153846
Min length10

Characters and Unicode

Total characters285
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row1475,933158
2nd row1663,807337
3rd row1649,653827
4th row1621,337067
5th row1613,038722

Common Values

ValueCountFrequency (%)
1688,4802791
 
3.1%
1919,9917651
 
3.1%
1894,1120591
 
3.1%
1891,7004261
 
3.1%
1881,4775481
 
3.1%
1874,6576881
 
3.1%
1796,2154521
 
3.1%
1829,6292011
 
3.1%
1824,0352981
 
3.1%
1796,6447891
 
3.1%
Other values (16)16
50.0%
(Missing)6
 
18.8%

Length

2022-06-24T16:30:16.574417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1688,4802791
 
3.8%
1919,9917651
 
3.8%
1649,6538271
 
3.8%
1621,3370671
 
3.8%
1613,0387221
 
3.8%
1599,0063311
 
3.8%
1616,1532041
 
3.8%
1633,3066981
 
3.8%
1626,4055251
 
3.8%
1611,3350791
 
3.8%
Other values (16)16
61.5%

Most occurring characters

ValueCountFrequency (%)
143
15.1%
633
11.6%
928
9.8%
328
9.8%
,26
9.1%
725
8.8%
824
8.4%
223
8.1%
019
6.7%
519
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number259
90.9%
Other Punctuation26
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
143
16.6%
633
12.7%
928
10.8%
328
10.8%
725
9.7%
824
9.3%
223
8.9%
019
7.3%
519
7.3%
417
 
6.6%
Other Punctuation
ValueCountFrequency (%)
,26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
143
15.1%
633
11.6%
928
9.8%
328
9.8%
,26
9.1%
725
8.8%
824
8.4%
223
8.1%
019
6.7%
519
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
143
15.1%
633
11.6%
928
9.8%
328
9.8%
,26
9.1%
725
8.8%
824
8.4%
223
8.1%
019
6.7%
519
6.7%

Interactions

2022-06-24T16:29:59.676483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.234028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.635670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.964968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.396288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.750591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.218920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.611231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.099564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.595899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.934198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.436535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.811842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.302176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.771504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.330050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.724690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.053988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.490309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.844612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.313941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.706253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.194585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.685919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.031220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.530555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.905863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.397197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.865525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.419070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.815711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.145008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.583330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.937633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.408962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.801274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.289607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.778940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.126241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.625577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.006886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.491218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.078573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.509090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.905731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.235028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.677353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.031654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.504984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.010320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.384628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.872961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.221263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.724599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.103908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.588240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.176595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.602111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.005753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.333050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.773372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.128676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.603006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.115344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.485651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.968982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.320285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.824621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.202930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.686261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.272616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.697132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.097774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.426071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.869394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.226698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.704028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.212366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.585673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.063003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.418307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.920643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.298951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.785284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.373639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.795154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.193796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.525093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.968416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.326720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.808051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.314389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.687696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.164026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.521330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.022666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.518000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.887306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.471661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.889175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.288817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.620115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.071439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.538768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.908074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.411410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.786718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.260048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.622352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.123688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.616022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.985328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.571683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:41.985196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.389839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.716136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.170461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.637790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.012097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.511433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.887740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.358069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.723375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.223711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.715044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.086351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.664704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.078217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.483860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:44.807156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.265483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.731811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.109119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.604453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.983762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.450090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:54.818396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.317732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.812066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.180372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.766727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.175239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.583883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.010202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.363505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.830833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.211142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.705476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.084785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.548112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.034445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.417754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:57.911088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.281395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.869750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.270260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.684905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.107223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.461527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:47.927855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.310164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.803498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.184807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.643133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.134467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.515776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.010110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.380417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:00.967772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.363610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.778926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.203245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.557548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.023876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.410186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:50.903520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.284829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.742156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.235490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.613798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.108133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.478439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:30:01.064794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:42.540649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:43.871947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:45.296266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:46.654570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:48.120898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:49.511209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:51.000542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:52.496877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:53.837177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:55.337512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:56.711820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:58.205154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T16:29:59.577461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-24T16:30:16.676439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-24T16:30:16.844477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-24T16:30:17.009514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-24T16:30:17.235564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-24T16:30:18.477842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-24T16:30:01.437877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-24T16:30:02.626143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-24T16:30:02.947215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

yearcivil_conflictsstate_interventionconflict_between_statestotalGDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD]GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU]GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA]GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA]GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN]GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN]GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS]GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA]GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR]GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA]GDP (current US$) [NY.GDP.MKTP.CD] - India [IND]GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX]GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF]GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN]GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD]Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU]Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA]Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR]Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX]Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF]Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN]Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD]
01970-01-01 00:00:00.0000019893352403,8965516924,3438610633,3883834544,858037686NaNNaN3,6726481592,5776026923,165,9473433284,1055093312,3947841594,2063343553,740605731,39897E+121,02521E+129,28661E+113,05491E+125,65056E+115,065E+115,64158E+129,26885E+113,47028E+112,96042E+112,21401E+11990308568253,47768E+112,01713E+132,5324305342,8799353122,026427320,9407810361,934614309NaN5,8712060084,0409543322,686253,5341797280,5172558294,3578847412,4471013153,444799933163160514529665454335176754865392796635354210747134689NaN3,21867E+113745664681387614869661058979692511533758284181886467112513326306,58524E+1111,213,69,910,214,914,616,413,625,53432,12129,35231,40821,5826,2738312475,0131463476,3487804976,819512278,8180487877,119512269,1717073275,0170731775,5829268365,94757,470,46963,03569,05465,247049314514,7047063795,9002352566,1089253352,0059327963,297905NaN7890,2872023626,297861993,9052667342,7009961426,6712582585,623273724,11610511475,933158
11970-01-01 00:00:00.0000019904432495,2550060832,9239350811,9857749434,892713066NaN-2,9999956421,8859649580,73375552-4,355,5334545635,175768386-0,3177856763,9202513682,8762450791,77167E+121,26918E+121,18122E+123,13282E+125,9393E+115,16814E+115,96314E+121,09317E+123,90726E+113,20979E+112,61254E+111,15552E+113,60858E+112,27395E+132,5184327072,8052676271,8807984130,9417917661,958793742NaN5,6051752943,9832176952,3638410253,146214610,4330810353,8963514782,4540161163,3104280093983466688035774430325207346255792880045168211414631847NaN3,25129E+11435450948409236296955105370354501210872502436445820499263492517,13011E+1111,413,410101513,416,713,924,84431,51728,80230,31221,0625,8787007975,227756176,676,9707317178,8368292777,4219512268,8860975675,2146341575,880487866,34357,86570,86663,30769,14565,433232834421,3645313846,6252572583,8880773552,8585827630,0923065941,5862567671,7730663597,051621940,9658549350,07573351473,388612471,598122766,99532941663,807337
21970-01-01 00:00:00.0000019914822525,1082615221,0481758471,5384475583,417496762NaN-5,046939451-0,108264787-1,1031216621,0321895851,0568314334,214754839-1,0182198739,2627860841,4464991341,86895E+121,26928E+121,24622E+123,58442E+126,10328E+115,17963E+116,15813E+121,1428E+123,42609E+112,70105E+113,13143E+111,23943E+113,83373E+112,3707E+131,9978349882,8121364381,8588526840,9408219041,895444339NaN4,8834293984,1168024231,9633333332,9099743280,4354023013,2226338942,3112434123,0107127673719672973035869120915215857067723278541575411338503299NaN2,99373E+1147045457327669466529586224738811459136041387441513598023753666,98285E+1110,413,29,99,914,412,116,213,824,24530,92428,2429,19419,6825,2113324575,319512276,8487804977,019512279,1007317177,6219512268,4743902475,3658536676,0829268366,74258,35371,24863,38469,24265,618380854302,8911754043,099162645,6732373574,0059297517,3303045870,2338577631,4678063707,951971942,0380381357,36676281518,4892772518,117842736,85180141649,653827
31970-01-01 00:00:00.0000019924441491,9230765591,5993426770,8342754540,848069581NaN-14,531073773,5224405230,401082076-0,5440720515,4823960223,541102416-2,13705688914,224529592,0561256112,13157E+121,40147E+121,32016E+123,90881E+125,92388E+114,60291E+116,52033E+121,17966E+123,28188E+112,88208E+113,63158E+111,34545E+114,26916E+112,53937E+131,8604799412,6906607111,7519769830,948312441,86168774,4270323214,9704668083,8647177661,5215288612,7048397060,4694546562,8176506152,4495420682,9429478113950245227137902269821221770903903599912357610788803124NaN3,25034E+11455925918184993804467808323141018245500663677406461122442678427,30299E+111013,1109,814,110,715,813,623,72130,34827,68128,09518,2724,578577375,819512277,177,419512279,1539024477,7243902466,8731707375,6170731776,4341463467,14158,85171,6163,24769,35565,770016584190,984223954,0032612627,3400083644,194767601,6851775356,509747677,4014013684,724535935,7464624363,07834481503,6781222290,666487752,62866251621,337067
41970-01-01 00:00:00.000001993376043-0,976849818-0,628666352-0,852805764-0,517919847NaN-8,6685403412,7517810422,4898309854,9246900054,750776221,9411558481,23351991313,88372931,8081666612,07132E+121,32282E+121,06496E+124,45414E+125,77171E+114,35084E+116,85856E+121,06139E+123,68296E+112,79296E+115,00736E+111,47197E+114,44731E+112,58225E+131,6935383232,6895348061,7867679730,9506638251,8217535044,1813246474,6043502953,5906495121,9279228152,8235422460,4427854942,4955236981,9280668742,76636001635030537589357752732931824201390141353936222102688226227.766720e+093,16719E+11381132067917099899155825354258121229803383254313379123602258616,98673E+119,8139,79,613,59,415,413,223,25229,79227,13427,05518,0924,1812043775,8707317177,377,7219512279,2936585477,8243902464,9358536675,419512276,3853658567,53959,34971,95362,89469,49665,88440364122,695744001,9022022611,9904383655,9668437770,5464485059,8646257709,4965893713,185819943,3269952364,51449941502,2156392395,381195788,12872361613,038722
51970-01-01 00:00:00.0000019944620482,3918920712,3583421812,1510236380,993066363NaN-12,569755984,0287932633,8460091685,8528703646,6589240674,9410806763,20000000313,036806633,2968990352,20507E+121,39398E+121,09922E+124,9988E+125,78139E+113,95077E+117,28724E+121,14049E+125,2537E+113,27276E+115,27813E+111,53513E+115,64325E+112,78724E+131,5502764332,660374961,713742760,92287181,6966802574,5237006554,2152646753,3819337182,0160077892,6646737270,5188303272,5619659011,6934795992,5798263053419770564037288606060180624590824528559408395773776401.354787e+103,08084E+11385690135051059149911888805512262635284079347858229198671200667,07808E+119,512,59,41013,39,5151322,82629,25826,60426,10717,723,8048759476,2707317177,6487804977,9219512279,6870731777,8268292764,4670731775,619512276,8853658567,93259,8472,27962,33169,6766,087772344088,798133836,1118342578,4659783844,286687957,2456334426,6681957757,8308223732,521114976,9821774371,22591411509,9393172420,083112816,162891599,006331
61970-01-01 00:00:00.0000019953721401,5441464962,1066952532,8868367592,630999616NaN-4,1435284062,6842172752,5316700054,2237936347,57449184-6,2912308213,10000000110,953954343,0893539022,58579E+121,60109E+121,17466E+125,54556E+126,04032E+113,95537E+117,63975E+121,34642E+127,69333E+113,60282E+113,60074E+111,71735E+117,34548E+113,10438E+131,4950220142,4924961891,4678431430,9168766171,5540900713,7844299673,8602457922,8541994911,862136982,5784835720,4508915312,1178640471,6862338872,3860503443874269910340124024138171858530044996167323791769039081.274163e+102,95853E+113829428308014318919608975446463015626153723292446562123851294757,26591E+119,412,89,29,5412,99,314,612,622,42428,74926,09625,27617,1223,3694584776,4219512277,7512195178,1707317179,5363414678,0292682964,6907317175,6219512276,8365853768,31860,3272,59861,56169,88566,274217264119,6889483981,3521752799,3757623934,9595617979,8984464290,6934677763,7551063728,963152994,2898492385,09185741437,6468812498,893625866,83437431616,153204
71970-01-01 00:00:00.0000019963632410,8058228911,4129936731,2667848023,133870993NaN-3,7550694393,7725655022,4285428952,2088640517,5495222496,7732586944,2999999979,9225567523,613661862,49724E+121,60568E+121,31243E+124,92339E+126,28546E+113,91725E+118,07312E+121,42151E+128,50426E+113,92897E+114,10976E+111,63237E+118,63747E+113,17363E+131,465858462,4144739511,5884922790,9112470761,4037525813,7565588213,5549822062,7245923221,65484352,4727682250,4764847781,7558787981,652726672,3162145923670118977238977734096207931288324404710468086158844711.582634e+102,87961E+113856613464514073226545990467273618828731032591787131142754008207,24515E+119,712,89,39,612,48,914,412,622,02228,26225,61124,57216,9823,0812395676,6731707377,9536585478,5219512280,200243978,180487865,8541463476,0268292777,0878048868,69560,78372,92560,59570,1466,558517524246,5531914191,9241842796,1547364008,4762368053,4548584252,6116197844,4682663879,8227861030,668072389,4265161445,9336532504,903115881,65373741633,306698
81970-01-01 00:00:00.0000019973541401,7921608212,3362965291,8302122340,981228732NaN1,3999158054,3817751744,909025743,3948459854,0498208496,8468522792,6000000019,2367798923,8690314932,21199E+121,45288E+121,24188E+124,49245E+126,54987E+114,04929E+118,57755E+121,55957E+128,83206E+114,15868E+115,00413E+111,68977E+119,61604E+113,16198E+131,4093017332,3754096531,6266927990,9204378981,2462432024,0398092973,4055622442,5601487721,5776883162,6477324260,4580958541,5821335571,6326506732,3210668843126771040334697904099201561022584063484060879451401831.757735e+102,93168E+1139889947449139342665521146488339021840610422414137710156995867697,24579E+119,912,79,49,511,78,614,212,521,60127,78925,14823,98316,5722,7507775277,0731707378,3048780578,8243902480,4241463478,4317073266,6987804976,4292682977,2109756169,06161,23373,26859,48970,42866,843390024203,3573674049,2331832834,4029464041,167678056,2436284069,6872927828,5810963760,7123651066,186951397,37818551498,0018822527,940174871,75632381626,405525
91970-01-01 00:00:00.0000019983262402,0139327853,5886594251,810615162-1,2703304952,79654248-5,2999616254,4814075613,1539733750,3380979026,1844158215,1639251680,57,8459517882,7931740312,23899E+121,50311E+121,27005E+124,09836E+126,34E+112,70955E+119,06282E+121,65339E+128,63711E+114,21351E+115,26502E+111,52983E+111,02904E+123,15397E+131,3908947862,2262083071,6445088790,9385947751,2562939022,7326490893,2015584992,4959622751,662291922,7273114170,4504504871,3831667231,6550806912,2704647173120076807833633561283208251253523784901264377486079847.955730e+092,90996E+1141222125833143573570471192061081822632234531905656009170317802007,06829E+119,612,89,49,611,48,814,312,321,14727,32424,70223,49915,6422,3376770877,4756097678,6048780578,9756097680,5014634178,6341463467,029756176,580487877,190243969,41961,66973,62558,31570,73767,086499214177,2941294151,627472912,9473653955,6276277948,3972783981,4994687803,6976053787,1084521075,199081399,49882921516,6108342471,477036869,35860731611,335079

Last rows

yearcivil_conflictsstate_interventionconflict_between_statestotalGDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Germany [DEU]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - France [FRA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Italy [ITA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Japan [JPN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Canada [CAN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Russian Federation [RUS]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United States [USA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - United Kingdom [GBR]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Brazil [BRA]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - India [IND]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - Mexico [MEX]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - South Africa [ZAF]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - China [CHN]GDP growth (annual %) [NY.GDP.MKTP.KD.ZG] - World [WLD]GDP (current US$) [NY.GDP.MKTP.CD] - Germany [DEU]GDP (current US$) [NY.GDP.MKTP.CD] - France [FRA]GDP (current US$) [NY.GDP.MKTP.CD] - Italy [ITA]GDP (current US$) [NY.GDP.MKTP.CD] - Japan [JPN]GDP (current US$) [NY.GDP.MKTP.CD] - Canada [CAN]GDP (current US$) [NY.GDP.MKTP.CD] - Russian Federation [RUS]GDP (current US$) [NY.GDP.MKTP.CD] - United States [USA]GDP (current US$) [NY.GDP.MKTP.CD] - United Kingdom [GBR]GDP (current US$) [NY.GDP.MKTP.CD] - Brazil [BRA]GDP (current US$) [NY.GDP.MKTP.CD] - India [IND]GDP (current US$) [NY.GDP.MKTP.CD] - Mexico [MEX]GDP (current US$) [NY.GDP.MKTP.CD] - South Africa [ZAF]GDP (current US$) [NY.GDP.MKTP.CD] - China [CHN]GDP (current US$) [NY.GDP.MKTP.CD] - World [WLD]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Germany [DEU]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - France [FRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Italy [ITA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Japan [JPN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Canada [CAN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Russian Federation [RUS]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United States [USA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - United Kingdom [GBR]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Brazil [BRA]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - India [IND]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - Mexico [MEX]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - South Africa [ZAF]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - China [CHN]Military expenditure (% of GDP) [MS.MIL.XPND.GD.ZS] - World [WLD]Military expenditure (current USD) [MS.MIL.XPND.CD] - Germany [DEU]Military expenditure (current USD) [MS.MIL.XPND.CD] - France [FRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Italy [ITA]Military expenditure (current USD) [MS.MIL.XPND.CD] - Japan [JPN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Canada [CAN]Military expenditure (current USD) [MS.MIL.XPND.CD] - Russian Federation [RUS]Military expenditure (current USD) [MS.MIL.XPND.CD] - United States [USA]Military expenditure (current USD) [MS.MIL.XPND.CD] - United Kingdom [GBR]Military expenditure (current USD) [MS.MIL.XPND.CD] - Brazil [BRA]Military expenditure (current USD) [MS.MIL.XPND.CD] - India [IND]Military expenditure (current USD) [MS.MIL.XPND.CD] - Mexico [MEX]Military expenditure (current USD) [MS.MIL.XPND.CD] - South Africa [ZAF]Military expenditure (current USD) [MS.MIL.XPND.CD] - China [CHN]Military expenditure (current USD) [MS.MIL.XPND.CD] - World [WLD]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Germany [DEU]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - France [FRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Italy [ITA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Japan [JPN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Canada [CAN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Russian Federation [RUS]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United States [USA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - United Kingdom [GBR]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Brazil [BRA]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - India [IND]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - Mexico [MEX]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - South Africa [ZAF]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - China [CHN]Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN] - World [WLD]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Germany [DEU]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - France [FRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Italy [ITA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Japan [JPN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Canada [CAN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Russian Federation [RUS]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United States [USA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - United Kingdom [GBR]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Brazil [BRA]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - India [IND]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - Mexico [MEX]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - South Africa [ZAF]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - China [CHN]Life expectancy at birth, total (years) [SP.DYN.LE00.IN] - World [WLD]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Germany [DEU]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - France [FRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Italy [ITA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Japan [JPN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Canada [CAN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Russian Federation [RUS]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United States [USA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - United Kingdom [GBR]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Brazil [BRA]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - India [IND]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - Mexico [MEX]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - South Africa [ZAF]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - China [CHN]Energy use (kg of oil equivalent per capita) [EG.USE.PCAP.KG.OE] - World [WLD]
221970-01-01 00:00:00.0000020113071383,9251927052,1927006330,7073333470,0238095243,1468813724,3000291861,5508355051,4575633913,9744230795,2413150013,663007933,1685562799,5508321793,3396178863,74931E+122,86516E+122,29499E+126,23315E+121,79333E+122,04593E+121,55426E+132,67489E+122,61616E+121,82305E+121,18049E+124,58202E+117,5515E+127,36715E+131,2061503361,8914062141,4759565970,9868064931,1932918953,4330438384,8401739952,5027264021,4118511592,7044835580,4657778031,1032576761,6655760532,41203338445163212878541208710103382880497160762213841213937208647.023752e+107,52288E+11665695525733693620989649633815794549845854245941540781,25286E+111,75048E+128,312,79,28,31112,612,712,815,1120,49519,76623,09713,2719,8546705380,4365853782,1146341582,1878048882,5912195181,4487804969,6839024478,6414634180,9512195173,92167,1375,01158,89574,70870,884017033869,8162293847,07222828,4048913610,8121697911,5545885049,4266317029,9546012972,1530651367,188049577,99442631587,0720712716,6811732085,0830221881,477548
231970-01-01 00:00:00.0000020122391330,4184975940,313134751-2,9809057681,3747509991,7622225494,0240861572,2495458521,4698875211,9211759855,4563887533,6423226792,3962323857,8637364492,6728184113,52714E+122,68367E+122,08696E+126,27236E+121,82837E+122,2083E+121,6197E+132,71916E+122,46523E+121,82764E+121,20109E+124,34401E+118,53223E+127,53116E+131,2416774791,87108061,4269249770,9674272781,1184045983,6892404354,4774012192,4205647311,3786564652,6181676190,4759872811,1327931121,6933681632,3728817943798225349502165074032978100820560011530195204521071118.146940e+107,25205E+11654524875543398700507447216920048571703557544895900961,45128E+111,75775E+128,412,698,21113,312,612,814,9319,92319,48822,81514,5719,9540581880,5390243981,9682926882,2390243983,0960975681,6487804970,0721951278,7414634180,9048780574,20967,54574,96660,0675,01371,173298993876,9481043836,6562992709,2977283537,363177733,4116555167,0103536872,0272843042,8598711413,733385599,15561981634,6974182636,6847262149,6025691891,700426
241970-01-01 00:00:00.0000020132790360,4375913030,576326675-1,8410654512,0051001772,3291225061,7554221491,8420810711,8900183423,004822676,3861064011,3540919622,4854680087,7661500982,8446516333,7338E+122,81188E+122,14192E+125,21233E+121,8466E+122,29247E+121,67848E+132,80329E+122,47282E+121,85672E+121,27444E+124,00886E+119,57041E+127,74432E+131,1852579011,8498759181,3990204190,950866051,00236723,8540425834,0466788792,2936402411,3294460842,5488256790,5079194551,1232742621,7028550962,3044194344242647303520014624482995744590549023932407185157312108.835290e+106,79229E+11638377248553287478723147403528801647314437841182084831,6407E+111,75559E+128,512,48,58,210,813,212,412,114,77219,41619,19822,48313,0319,4524478480,490243982,219512282,690243983,3319512281,7487804970,5787804978,7414634181,0048780574,48367,93174,9361,09975,32171,465864413939,5295633833,5342592579,4725433567,6293547743,7257425078,6263426905,5986332987,7005891461,076774605,79403781616,6136452602,8455982204,2432991894,112059
251970-01-01 00:00:00.00000201428131422,2095434310,956183052-0,0045475420,2962055142,8700360750,7362672212,5259734462,9911648140,503955747,4102276052,8497732551,4138264527,4257636563,1177528173,88909E+122,85596E+122,16201E+124,89699E+121,80575E+122,05924E+121,75272E+133,08717E+122,45604E+122,03913E+121,31535E+123,81199E+111,04757E+137,95755E+131,1499419971,8629614391,2829696540,9669993340,9899252994,1129929783,695894652,1839062481,3302444232,5439825030,5138299571,1094378831,7286890682,25108875144662831168531347508992770103433546903466613178536404788.469650e+106,47789E+11669954686543266023936950914096277675869384538924850911,82109E+111,75419E+128,812,48,3810,813,312,51214,62418,98418,89222,11313,8319,4641918681,090243982,719512283,090243983,5878048881,870,7436585478,8414634181,3048780574,74568,28674,90861,96875,62971,746054733779,4619213659,0877952414,4840023470,7631297897,8556154942,8754836960,6839972777,3109871495,541141636,5718341561,8733422695,5057762224,3548981919,991765
261970-01-01 00:00:00.00000201531201521,4919315281,1129123410,7783043511,5606266970,659176864-1,9727192263,075514652,622596679-3,5457633937,9962537863,2931515281,3218622377,0413288793,1683024483,35759E+122,43919E+121,83664E+124,44493E+121,55651E+121,36348E+121,82383E+132,95657E+121,80221E+122,10359E+121,17187E+123,4671E+111,10616E+137,51171E+131,1373384271,8722582811,2082283260,959251331,1527093744,8715147473,4778451662,0455031921,3655171542,4574506010,4666761221,0991820751,7507177672,25509805638170021071456474716412218084507042106103306179376418956.642182e+106,3383E+11599902057192461770168351295483754546883781234888679481,96539E+111,65293E+129128810,713,312,411,914,47218,62518,57321,71911,9918,9522252780,6414634182,3219512282,5439024483,7939024481,971,1834146378,690243980,9560975674,99468,60774,90462,64975,92871,952114723817,5494153692,0178332481,7546453428,5572167631,34157NaN6803,9966072764,516671NaNNaN1537,261412NaNNaNNaN
271970-01-01 00:00:00.00000201634182542,2299998681,0954644041,2934627320,7538267461,0013944140,1936900721,7114267742,263463538-3,2759169068,2563055022,6305324250,6645523086,8487622052,8250572883,46985E+122,47296E+121,87707E+125,00368E+121,52799E+121,27679E+121,87451E+132,72285E+121,79569E+122,2948E+121,07849E+123,23586E+111,12333E+137,63132E+131,1496544851,9172823681,3348352520,9448382861,1641615675,4251477053,4189423371,9814382551,3479754132,5431511880,4950644141,0651996451,7706957192,21785335239855047309473705895532503302789546471287714177827755436.924529e+106,39856E+11533273276342422474690156637622641533687574031393121281,98538E+111,64914E+129,611,87,87,810,612,912,211,814,30718,33218,24521,31413,5719,0710172480,990243982,5731707383,2439024483,9848780581,971,6512195178,5390243981,1560975675,2368,89774,91763,15376,2172,18610065NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
281970-01-01 00:00:00.00000201729211512,6802311142,2914199941,6678590411,6753317523,0398802251,8257900642,3326793952,1344530931,3228690546,7953834192,1131291351,1579469526,9472007933,3943743373,69085E+122,59515E+121,9618E+124,93084E+121,64927E+121,5742E+121,9543E+132,69902E+122,06351E+122,65147E+121,15891E+123,81449E+111,23104E+138,12246E+131,1540494071,9086420121,3573705590,9319802591,3516022324,2489960523,3133812941,9463249961,4144856112,5314625880,4365102961,0300605521,746455072,17219832642210258091491956622502644789291545387031802222696963236.691303e+106,46753E+11516335392172926183309764559435281506207664635915076132,10443E+111,71798E+129,511,57,67,610,311,511,811,414,12518,08317,91820,90812,6418,6653470580,9926829382,5756097682,9463414684,099756181,972,4514634178,5390243981,2560975675,45669,16574,94763,53876,4772,39140625NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
291970-01-01 00:00:00.00000201831192521,0860245141,8650660710,9258109410,5588512752,7770405542,807245412,9964643521,6509254961,7836667616,5329890112,1949947251,4876173736,7497738323,2695836413,97729E+122,79096E+122,09193E+125,03689E+121,72533E+121,65733E+122,06119E+132,90079E+121,91693E+122,70111E+121,22241E+124,04842E+111,38948E+138,62743E+131,1718167921,8450533991,3595427510,9409936921,3246810943,6925187593,3162488081,9487557081,4942033532,4339220670,4775174070,9849613981,7395336832,14812066846423020903514098128392842009843846617954864227293275806.160920e+106,82491E+11556802282152817740687266257801718583952127136229187432,32531E+111,80195E+129,511,37,37,410,110,911,61113,92417,85717,60220,5110,8618,1488114680,8926829382,6756097683,3463414684,2109756182,0487804972,6621951278,6390243981,2560975675,67269,41674,99263,85776,70472,57398037NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
301970-01-01 00:00:00.00000201928252551,0555082471,8429718140,4102782940,2703046451,8795920282,0329827392,1611765151,6719441971,4111529854,041554187-0,1765994560,1130536975,9497142332,6008142443,88833E+122,72887E+122,00938E+125,14878E+121,74202E+121,68745E+122,14332E+132,87867E+121,87782E+122,8705E+121,26943E+123,87935E+111,42799E+138,75681E+131,2691468671,8454911961,3165669780,9372810871,278941423,8321156813,4270801812,0130045731,4079885162,5191207640,523482490,9780573971,7278278632,1868637649007512315501189292122638067398147609019987222044084406.520134e+107,34344E+11568561330662590687119771468900524665080825434357360372,40333E+111,85993E+129,411,2779,910,111,410,713,70317,64417,29720,12910,4117,8782938181,2926829382,8268292783,4975609884,3563414682,0487804973,0839024478,7878048881,2048780575,88169,65675,05464,13176,91272,76266676NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
311970-01-01 00:00:00.0000020202825356-4,569616717-7,855256037-8,9385925-4,585508281-5,233024303-2,951273899-3,404591573-9,396160006-4,059048273-7,251754782-8,309034659-6,4319748262,347513573-3,293683043,84641E+122,63032E+121,88871E+125,05776E+121,64542E+121,4835E+122,0953E+132,7598E+121,44473E+122,66025E+121,07392E+123,35442E+111,47227E+138,4747E+131,3997303682,0733214271,5675248910,9968254381,4150558414,2634498363,7411600912,2465466791,4391682262,88267040,5736516591,0741934431,7497957732,36247261652764761199527470648582892134275649148557003227548471296.171254e+107,78232E+11592384622501973634775972887446604611637658231508288802,52304E+111,92885E+129,310,96,86,89,49,810,910,213,46317,43717,00519,7738,5217,3342150280,9414634182,1756097682,3439024484,6156097681,7487804971,3387804977,280487880,9024390276,08469,88775,13164,37977,09772,74730314NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN